A Basic Time Series Forecasting Course with Python

Alain Zemkoho ()
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Alain Zemkoho: University of Southampton

SN Operations Research Forum, 2023, vol. 4, issue 1, 1-43

Abstract: 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.

Keywords: Python; Forecasting; Exponential smoothing; ARIMA; Regression; 90-04; 97U50; 97U70 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s43069-022-00179-z

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