https://github.com/alro10/classical-time-series-forecasting
Coding from classical methods applying in time series forecasting
https://github.com/alro10/classical-time-series-forecasting
arima arima-forecasting python sarimax series-forecasting time-series time-series-forecasting time-series-prediction
Last synced: 7 months ago
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Coding from classical methods applying in time series forecasting
- Host: GitHub
- URL: https://github.com/alro10/classical-time-series-forecasting
- Owner: Alro10
- License: apache-2.0
- Created: 2019-10-02T20:02:09.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-11-08T20:23:03.000Z (almost 6 years ago)
- Last Synced: 2025-01-20T13:27:28.594Z (9 months ago)
- Topics: arima, arima-forecasting, python, sarimax, series-forecasting, time-series, time-series-forecasting, time-series-prediction
- Language: Jupyter Notebook
- Size: 497 KB
- Stars: 4
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Classical Methods for Time Series Forecasting
Coding, Python and R, from classical methods applying in time series forecasting
## Resources
- [An Introductory Study on Time Series Modeling and Forecasting](https://arxiv.org/pdf/1302.6613.pdf)
- https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/
- https://towardsdatascience.com/machine-learning-part-19-time-series-and-autoregressive-integrated-moving-average-model-arima-c1005347b0d7
- https://towardsdatascience.com/econometric-approach-to-time-series-analysis-seasonal-arima-in-python-28f5782ee23
## Methods
- Moving Average
- ARMA
- ARIMA
- [SARIMA](https://github.com/Alro10/classical-time-series-forecasting/blob/master/SARIMA.ipynb)
- The ```statsmodels``` implementation called this SARIMAX because the “X” addition to the method name means that the implementation also supports exogenous variables.
## Packages
- [Python-SARIMAX](https://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html)
- [R- Time series Analysis](http://db.ucsd.edu/static/TimeSeries.pdf)