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A Basic Time Series Forecasting Course with Python

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  • Alain Zemkoho

    (University of Southampton)

Abstract

The aim of this paper is to present a set of Python-based tools to develop forecasts using time series data sets. The material is based on a 4-week course that the author has taught for 7 years to students on operations research, management science, analytics, and statistics 1-year MSc programmes. However, it can easily be adapted to various other audiences, including executive management or some undergraduate programmes. No particular knowledge of Python is required to use this material. Nevertheless, we assume a good level of familiarity with standard statistical forecasting methods such as exponential smoothing, autoregressive integrated moving average (ARIMA), and regression-based techniques, which is required to deliver such a course. Access to relevant data, codes, and lecture notes, which serve as based for this material, is made available (see https://github.com/abzemkoho/forecasting ) for anyone interested in teaching such a course or developing some familiarity with the mathematical background of relevant methods and tools.

Suggested Citation

  • Alain Zemkoho, 2023. "A Basic Time Series Forecasting Course with Python," SN Operations Research Forum, Springer, vol. 4(1), pages 1-43, March.
    • Handle: RePEc:spr:snopef:v:4:y:2023:i:1:d:10.1007_s43069-022-00179-z
    DOI: 10.1007/s43069-022-00179-z
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    References listed on IDEAS

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    1. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    2. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2018. "The M4 Competition: Results, findings, conclusion and way forward," International Journal of Forecasting, Elsevier, vol. 34(4), pages 802-808.
    3. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    4. Chatfield, Chris, 1993. "Neural networks: Forecasting breakthrough or passing fad?," International Journal of Forecasting, Elsevier, vol. 9(1), pages 1-3, April.
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    Cited by:

    1. Bisheng He & Yongjun Zhu & Andrea D’Ariano & Keyu Wen & Lufeng Chen, 2023. "Dynamic Relational Graph Convolutional Network for Metro Passenger Flow Forecasting," SN Operations Research Forum, Springer, vol. 4(4), pages 1-27, December.

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