工具 / statsmodels
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###### tags: `ML / 時間序列`
###### tags: `ML`, `時間序列`, `statsmodels`

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## [Autoregressions](https://www.statsmodels.org/stable/examples/notebooks/generated/autoregressions.html)
> 資料來源：[Time series forecasting with FEDOT. Guide](https://github.com/ITMO-NSS-team/fedot-examples/blob/main/notebooks/latest/3_intro_ts_forecasting.ipynb)

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## VAR
> 資料來源：[A Multivariate Time Series Guide to Forecasting and Modeling (with Python codes)](https://www.analyticsvidhya.com/blog/2018/09/multivariate-time-series-guide-forecasting-modeling-python-codes/)
> #multivariate(多變量), Vector Auto Regression

### 測試集：每月搭機旅客人數 (AutoML)

y_pred_var = [504.38935402, 503.78798811, 503.19576126, 502.61253456,
       502.03817125, 501.47253662, 500.91549802, 500.36692482,
       499.82668837, 499.29466198, 498.77072087, 498.25474217,
       497.74660487, 497.24618981, 496.75337964, 496.26805877,
       495.7901134 , 495.31943143, 494.85590249, 494.39941787,
       493.94987051, 493.50715499, 493.07116748, 492.64180574,
       492.21896907, 491.80255832, 491.39247582, 490.9886254 ]


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## Holt-Winter
### ExponentialSmoothing
- doc: [statsmodels.tsa.holtwinters.ExponentialSmoothing](https://www.statsmodels.org/dev/generated/statsmodels.tsa.holtwinters.ExponentialSmoothing.html)
- [api](https://docs.w3cub.com/statsmodels/generated/statsmodels.tsa.holtwinters.exponentialsmoothing)

### Holt
- doc: [statsmodels.tsa.holtwinters.Holt](https://www.statsmodels.org/dev/generated/statsmodels.tsa.holtwinters.Holt.html)
- [api](https://docs.w3cub.com/statsmodels/generated/statsmodels.tsa.holtwinters.holt)

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## Trend and seasonality
- ### [An Introduction to Time Series Forecasting with Python](https://www.researchgate.net/publication/324889271_An_Introduction_to_Time_Series_Forecasting_with_Python)
- [pycon-ua-2018/look-into-the-data.ipynb](https://github.com/gakhov/pycon-ua-2018/blob/master/look-into-the-data.ipynb)