Prediction intervals for machine learning models

[narencastellon]

Description

Checklist:

• [x] This PR has a meaningful title and a clear description.
• [ ] The tests pass.
• [ ] All linting tasks pass.
• [x] The notebooks are clean.

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[jmoralez]

Hey @Naren8520, can you please rename this notebook to use underscores (prediction_intervals_in_forecasting_models), move it to the nbs/docs folder and add it to the nbs/sidebar.yml file?

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jmoralez commented on 2023-09-06T22:14:26Z ----------------------------------------------------------------

This should be "Installing mlforecast"

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:27Z ----------------------------------------------------------------

I think this can be deleted

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jmoralez commented on 2023-09-06T22:14:28Z ----------------------------------------------------------------

We don't have statsmodels as a dependency so this will likely fail if it was tested. You can add the statsmodels library to the environment.yml file

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jmoralez commented on 2023-09-06T22:14:29Z ----------------------------------------------------------------

plotly is not used so please remove those imports. also the rcParams is available in matplotlib, so you could use plt.rcParams instead (to remove the pylab import)

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jmoralez commented on 2023-09-06T22:14:29Z ----------------------------------------------------------------

This isn't exactly true. We need those three columns but they can have other names. Also the ds can be an integer.

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jmoralez commented on 2023-09-06T22:14:30Z ----------------------------------------------------------------

You can avoid this by using parse_dates=['ds'] in the read command above

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jmoralez commented on 2023-09-06T22:14:31Z ----------------------------------------------------------------

You can delete this cell as well if you use the parse_dates

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jmoralez commented on 2023-09-06T22:14:32Z ----------------------------------------------------------------

Please use utilsforecast.plotting.plot_series here.

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jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

It's preferable to use snake_case everywhere, so please rename this to augmented_dickey_fuller_test

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jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

please move this import to the start of the notebook

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jmoralez commented on 2023-09-06T22:14:34Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

remove this import

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jmoralez commented on 2023-09-06T22:14:35Z ----------------------------------------------------------------

This can be achieved with utilsforecast.plotting.plot_series(train, test)

Naren8520 commented on 2023-09-07T20:23:08Z ----------------------------------------------------------------

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:36Z ----------------------------------------------------------------

I think this could be renamed to something like "Modeling with MLForecast"

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jmoralez commented on 2023-09-06T22:14:37Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:38Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

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jmoralez commented on 2023-09-06T22:14:27Z ----------------------------------------------------------------

I think this can be deleted

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:28Z ----------------------------------------------------------------

We don't have statsmodels as a dependency so this will likely fail if it was tested. You can add the statsmodels library to the environment.yml file

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:29Z ----------------------------------------------------------------

plotly is not used so please remove those imports. also the rcParams is available in matplotlib, so you could use plt.rcParams instead (to remove the pylab import)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:29Z ----------------------------------------------------------------

This isn't exactly true. We need those three columns but they can have other names. Also the ds can be an integer.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:30Z ----------------------------------------------------------------

You can avoid this by using parse_dates=['ds'] in the read command above

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:31Z ----------------------------------------------------------------

You can delete this cell as well if you use the parse_dates

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:32Z ----------------------------------------------------------------

Please use utilsforecast.plotting.plot_series here.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

It's preferable to use snake_case everywhere, so please rename this to augmented_dickey_fuller_test

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

please move this import to the start of the notebook

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:34Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

remove this import

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:35Z ----------------------------------------------------------------

This can be achieved with utilsforecast.plotting.plot_series(train, test)

Naren8520 commented on 2023-09-07T20:23:08Z ----------------------------------------------------------------

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:36Z ----------------------------------------------------------------

I think this could be renamed to something like "Modeling with MLForecast"

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:37Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:38Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

View entire conversation on ReviewNB

• © Githubissues.
• Githubissues is a development platform for aggregating issues.

[review-notebook-app[bot]]

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jmoralez commented on 2023-09-06T22:14:28Z ----------------------------------------------------------------

We don't have statsmodels as a dependency so this will likely fail if it was tested. You can add the statsmodels library to the environment.yml file

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:29Z ----------------------------------------------------------------

plotly is not used so please remove those imports. also the rcParams is available in matplotlib, so you could use plt.rcParams instead (to remove the pylab import)

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:29Z ----------------------------------------------------------------

This isn't exactly true. We need those three columns but they can have other names. Also the ds can be an integer.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:30Z ----------------------------------------------------------------

You can avoid this by using parse_dates=['ds'] in the read command above

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:31Z ----------------------------------------------------------------

You can delete this cell as well if you use the parse_dates

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:32Z ----------------------------------------------------------------

Please use utilsforecast.plotting.plot_series here.

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

It's preferable to use snake_case everywhere, so please rename this to augmented_dickey_fuller_test

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

please move this import to the start of the notebook

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:34Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

remove this import

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:35Z ----------------------------------------------------------------

This can be achieved with utilsforecast.plotting.plot_series(train, test)

Naren8520 commented on 2023-09-07T20:23:08Z ----------------------------------------------------------------

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:36Z ----------------------------------------------------------------

I think this could be renamed to something like "Modeling with MLForecast"

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:37Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:38Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

View entire conversation on ReviewNB

• © Githubissues.
• Githubissues is a development platform for aggregating issues.

[review-notebook-app[bot]]

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:29Z ----------------------------------------------------------------

plotly is not used so please remove those imports. also the rcParams is available in matplotlib, so you could use plt.rcParams instead (to remove the pylab import)

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:29Z ----------------------------------------------------------------

This isn't exactly true. We need those three columns but they can have other names. Also the ds can be an integer.

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:30Z ----------------------------------------------------------------

You can avoid this by using parse_dates=['ds'] in the read command above

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:31Z ----------------------------------------------------------------

You can delete this cell as well if you use the parse_dates

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:32Z ----------------------------------------------------------------

Please use utilsforecast.plotting.plot_series here.

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

It's preferable to use snake_case everywhere, so please rename this to augmented_dickey_fuller_test

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

please move this import to the start of the notebook

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:34Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

remove this import

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:35Z ----------------------------------------------------------------

This can be achieved with utilsforecast.plotting.plot_series(train, test)

Naren8520 commented on 2023-09-07T20:23:08Z ----------------------------------------------------------------

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:36Z ----------------------------------------------------------------

I think this could be renamed to something like "Modeling with MLForecast"

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:37Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:38Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

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jmoralez commented on 2023-09-06T22:14:29Z ----------------------------------------------------------------

This isn't exactly true. We need those three columns but they can have other names. Also the ds can be an integer.

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jmoralez commented on 2023-09-06T22:14:30Z ----------------------------------------------------------------

You can avoid this by using parse_dates=['ds'] in the read command above

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jmoralez commented on 2023-09-06T22:14:31Z ----------------------------------------------------------------

You can delete this cell as well if you use the parse_dates

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jmoralez commented on 2023-09-06T22:14:32Z ----------------------------------------------------------------

Please use utilsforecast.plotting.plot_series here.

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jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

It's preferable to use snake_case everywhere, so please rename this to augmented_dickey_fuller_test

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jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

please move this import to the start of the notebook

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jmoralez commented on 2023-09-06T22:14:34Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

remove this import

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jmoralez commented on 2023-09-06T22:14:35Z ----------------------------------------------------------------

This can be achieved with utilsforecast.plotting.plot_series(train, test)

Naren8520 commented on 2023-09-07T20:23:08Z ----------------------------------------------------------------

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

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jmoralez commented on 2023-09-06T22:14:36Z ----------------------------------------------------------------

I think this could be renamed to something like "Modeling with MLForecast"

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jmoralez commented on 2023-09-06T22:14:37Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

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jmoralez commented on 2023-09-06T22:14:38Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

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jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

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jmoralez commented on 2023-09-06T22:14:30Z ----------------------------------------------------------------

You can avoid this by using parse_dates=['ds'] in the read command above

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jmoralez commented on 2023-09-06T22:14:31Z ----------------------------------------------------------------

You can delete this cell as well if you use the parse_dates

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jmoralez commented on 2023-09-06T22:14:32Z ----------------------------------------------------------------

Please use utilsforecast.plotting.plot_series here.

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jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

It's preferable to use snake_case everywhere, so please rename this to augmented_dickey_fuller_test

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jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

please move this import to the start of the notebook

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jmoralez commented on 2023-09-06T22:14:34Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

remove this import

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jmoralez commented on 2023-09-06T22:14:35Z ----------------------------------------------------------------

This can be achieved with utilsforecast.plotting.plot_series(train, test)

Naren8520 commented on 2023-09-07T20:23:08Z ----------------------------------------------------------------

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:36Z ----------------------------------------------------------------

I think this could be renamed to something like "Modeling with MLForecast"

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jmoralez commented on 2023-09-06T22:14:37Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

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jmoralez commented on 2023-09-06T22:14:38Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

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jmoralez commented on 2023-09-06T22:14:31Z ----------------------------------------------------------------

You can delete this cell as well if you use the parse_dates

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jmoralez commented on 2023-09-06T22:14:32Z ----------------------------------------------------------------

Please use utilsforecast.plotting.plot_series here.

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jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

It's preferable to use snake_case everywhere, so please rename this to augmented_dickey_fuller_test

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jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

please move this import to the start of the notebook

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jmoralez commented on 2023-09-06T22:14:34Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

remove this import

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jmoralez commented on 2023-09-06T22:14:35Z ----------------------------------------------------------------

This can be achieved with utilsforecast.plotting.plot_series(train, test)

Naren8520 commented on 2023-09-07T20:23:08Z ----------------------------------------------------------------

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:36Z ----------------------------------------------------------------

I think this could be renamed to something like "Modeling with MLForecast"

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jmoralez commented on 2023-09-06T22:14:37Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

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jmoralez commented on 2023-09-06T22:14:38Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

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• Githubissues is a development platform for aggregating issues.

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jmoralez commented on 2023-09-06T22:14:32Z ----------------------------------------------------------------

Please use utilsforecast.plotting.plot_series here.

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jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

It's preferable to use snake_case everywhere, so please rename this to augmented_dickey_fuller_test

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jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

please move this import to the start of the notebook

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jmoralez commented on 2023-09-06T22:14:34Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

remove this import

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:35Z ----------------------------------------------------------------

This can be achieved with utilsforecast.plotting.plot_series(train, test)

Naren8520 commented on 2023-09-07T20:23:08Z ----------------------------------------------------------------

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:36Z ----------------------------------------------------------------

I think this could be renamed to something like "Modeling with MLForecast"

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:37Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:38Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

View entire conversation on ReviewNB

• © Githubissues.
• Githubissues is a development platform for aggregating issues.

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jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

It's preferable to use snake_case everywhere, so please rename this to augmented_dickey_fuller_test

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

please move this import to the start of the notebook

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:34Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

remove this import

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:35Z ----------------------------------------------------------------

This can be achieved with utilsforecast.plotting.plot_series(train, test)

Naren8520 commented on 2023-09-07T20:23:08Z ----------------------------------------------------------------

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:36Z ----------------------------------------------------------------

I think this could be renamed to something like "Modeling with MLForecast"

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:37Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:38Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

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jmoralez commented on 2023-09-06T22:14:33Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

please move this import to the start of the notebook

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jmoralez commented on 2023-09-06T22:14:34Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

remove this import

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jmoralez commented on 2023-09-06T22:14:35Z ----------------------------------------------------------------

This can be achieved with utilsforecast.plotting.plot_series(train, test)

Naren8520 commented on 2023-09-07T20:23:08Z ----------------------------------------------------------------

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

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jmoralez commented on 2023-09-06T22:14:36Z ----------------------------------------------------------------

I think this could be renamed to something like "Modeling with MLForecast"

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jmoralez commented on 2023-09-06T22:14:37Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

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jmoralez commented on 2023-09-06T22:14:38Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

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jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

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jmoralez commented on 2023-09-06T22:14:34Z ----------------------------------------------------------------

Line #1.    from statsmodels.tsa.seasonal import seasonal_decompose 

remove this import

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jmoralez commented on 2023-09-06T22:14:35Z ----------------------------------------------------------------

This can be achieved with utilsforecast.plotting.plot_series(train, test)

Naren8520 commented on 2023-09-07T20:23:08Z ----------------------------------------------------------------

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:36Z ----------------------------------------------------------------

I think this could be renamed to something like "Modeling with MLForecast"

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jmoralez commented on 2023-09-06T22:14:37Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

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jmoralez commented on 2023-09-06T22:14:38Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

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jmoralez commented on 2023-09-06T22:14:35Z ----------------------------------------------------------------

This can be achieved with utilsforecast.plotting.plot_series(train, test)

Naren8520 commented on 2023-09-07T20:23:08Z ----------------------------------------------------------------

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:36Z ----------------------------------------------------------------

I think this could be renamed to something like "Modeling with MLForecast"

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:37Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:38Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

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• Githubissues is a development platform for aggregating issues.

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jmoralez commented on 2023-09-06T22:14:36Z ----------------------------------------------------------------

I think this could be renamed to something like "Modeling with MLForecast"

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:37Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:38Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

View entire conversation on ReviewNB

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• Githubissues is a development platform for aggregating issues.

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jmoralez commented on 2023-09-06T22:14:37Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:38Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

View entire conversation on ReviewNB

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• Githubissues is a development platform for aggregating issues.

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jmoralez commented on 2023-09-06T22:14:38Z ----------------------------------------------------------------

Please move these imports to the start of the notebook as well, it makes it easier to see which dependencies are needed.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

View entire conversation on ReviewNB

• © Githubissues.
• Githubissues is a development platform for aggregating issues.

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

Line #2.               ]

Move this bracket to the previous line

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

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jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

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jmoralez commented on 2023-09-06T22:14:39Z ----------------------------------------------------------------

If the data frequency is 30 min the first difference would subtract the value from the previous 30 min, not the previous day. Also in the plot above there doesn't seem to be a trend.

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

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jmoralez commented on 2023-09-06T22:14:40Z ----------------------------------------------------------------

Line #2.                     freq='30min',  # Dayly

Remove the comment, the frequency isn't daily

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

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jmoralez commented on 2023-09-06T22:14:41Z ----------------------------------------------------------------

This function isn't used in the model, either remove it or use it

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

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• Githubissues is a development platform for aggregating issues.

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jmoralez commented on 2023-09-06T22:14:42Z ----------------------------------------------------------------

There's no need to assign it back, so you can just use mlf.fit, also please put each argument in a separate line so it's easier to see them. Since the column names are the default ones you can remove those arguments as well (id_col, time_col, target_col)

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

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• © Githubissues.
• Githubissues is a development platform for aggregating issues.

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jmoralez commented on 2023-09-06T22:14:43Z ----------------------------------------------------------------

These should be in the same plot. If you remove the differences argument (which I don't think we need in this example) they will be on the same scale. Also please use matplotlib (we don't have seaborn listed as a dependency)

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

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• © Githubissues.
• Githubissues is a development platform for aggregating issues.

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

This should be something like "Producing prediction intervals" given that the intervals aren't confidence intervals

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

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• © Githubissues.
• Githubissues is a development platform for aggregating issues.

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jmoralez commented on 2023-09-06T22:14:44Z ----------------------------------------------------------------

Line #2.    preds = mlf.predict(h=30, level=levels)# 

Remove the comment at the end

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

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• © Githubissues.
• Githubissues is a development platform for aggregating issues.

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jmoralez commented on 2023-09-06T22:14:45Z ----------------------------------------------------------------

Delete this cell, it's the same as the one below

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

View entire conversation on ReviewNB

• © Githubissues.
• Githubissues is a development platform for aggregating issues.

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jmoralez commented on 2023-09-06T22:14:46Z ----------------------------------------------------------------

Delete this cell

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

View entire conversation on ReviewNB

• © Githubissues.
• Githubissues is a development platform for aggregating issues.

[review-notebook-app[bot]]

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jmoralez commented on 2023-09-06T22:14:47Z ----------------------------------------------------------------

use the plot_series function here as well

review-notebook-app[bot] commented 1 year ago

View / edit / reply to this conversation on ReviewNB

jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

View entire conversation on ReviewNB

• © Githubissues.
• Githubissues is a development platform for aggregating issues.

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jmoralez commented on 2023-09-06T22:14:48Z ----------------------------------------------------------------

The Nixtla Parameters points to the arch model. I think we can delete that one.

narencastellon commented 1 year ago

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

View entire conversation on ReviewNB

• © Githubissues.
• Githubissues is a development platform for aggregating issues.

[narencastellon]

I think the function does not display both sets, or if it displays it, it cannot be seen because of the color

---

View entire conversation on ReviewNB