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facebook / prophet

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
https://facebook.github.io/prophet
MIT License
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In last line of doc , its given that '5m min resolution' and in code freq is given as ' freq='H' ' is it right ? #717

Closed multinucliated closed 5 years ago

multinucliated commented 5 years ago

https://facebook.github.io/prophet/docs/non-daily_data.html

Prophet can make forecasts for time series with sub-daily observations by passing in a dataframe with timestamps in the ds column. The format of the timestamps should be YYYY-MM-DD HH:MM:SS - see the example csv here. When sub-daily data are used, daily seasonality will automatically be fit. Here we fit Prophet to data with 5-minute resolution (daily temperatures at Yosemite):

# Python df = pd.read_csv('../examples/example_yosemite_temps.csv') m = Prophet(changepoint_prior_scale=0.01).fit(df) future = m.make_future_dataframe(periods=300, freq='H') fcst = m.predict(future) fig = m.plot(fcst)

Gynii commented 5 years ago

Yes i saw this also , they did it in hourly frequency but you still can do it in interval of 5 min

multinucliated commented 5 years ago

Thanks @Gynii ,I solved it like this

future = model.make_future_dataframe(periods=20, freq='15min')

for 15min interval and 20 more counts.