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**Is your feature request related to a problem? Please describe.**
In some cases where models are very slow, you may wish to refresh a model with new data without refitting its' parameters. I thought…
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### Describe the bug
If the data provided has numbers of different orders, auto_arima gives errors.
### To Reproduce
```
import numpy as np
import pmdarima as pm
# original data
data = np.arr…
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Sentiment Analysis:
Using transformer-based models like BERT or DistilBERT for sentiment analysis is a good choice, especially considering their effectiveness in capturing contextual information. I…
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The interpolation result by na_kalman() is pretty good when the option model='auto.arima' is used. Is it possible to show the searched parameter results of auto.arima()?
Did the package use the def…
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Hi all,
I am currently modeling time-series data of channel sales using auto-ARIMA. I need to add exogeneous variables to the ARIMA model. The variables are inflation, unemployment rate. I don't see t…
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This analysis is similar to #63, but here we use a more complex ARIMA model.
![image](https://user-images.githubusercontent.com/4161918/95064956-b3086b80-0700-11eb-8ab7-3b770a52ce23.png)
Some ex…
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**Is your feature request related to a problem? Please describe.**
In time series, many libraries provide the same models. Example,
AutoARIMA is available from pmdarima and a faster variant is …
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``` r
library(tidyverse)
#> Warning: package 'tidyverse' was built under R version 4.2.3
#> Warning: package 'tibble' was built under R version 4.2.3
#> Warning: package 'dplyr' was built under R …
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**Is your feature request related to a problem? Please describe.**
ARFIMA and various models for fractionally differenced models are useful in modelling low signal-to-noise ratio time series. Imple…
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Using the latest github version of fable here (but issue is also present on CRAN version).
``` r
library(tsibble)
library(fable)
#> Loading required package: fabletools
library(dplyr)
#>
#> …