A basic time series forecasting course with Python
A basic time series forecasting course with Python
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.
90-04, 97U50, 97U70, ARIMA, Exponential smoothing, Forecasting, Python, Regression
Zemkoho, Alain
30c79e30-9879-48bd-8d0b-e2fbbc01269e
1 March 2023
Zemkoho, Alain
30c79e30-9879-48bd-8d0b-e2fbbc01269e
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.
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Accepted/In Press date: 16 November 2022
e-pub ahead of print date: 23 December 2022
Published date: 1 March 2023
Additional Information:
Funding Information:
This work is supported by the EPSRC grant with reference EP/V049038/1 and the Alan Turing Institute under the EPSRC grant EP/N510129/1.
Keywords:
90-04, 97U50, 97U70, ARIMA, Exponential smoothing, Forecasting, Python, Regression
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Local EPrints ID: 474384
URI: http://eprints.soton.ac.uk/id/eprint/474384
PURE UUID: 94f299fd-5408-4828-9dbb-26c4309d5cd3
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Date deposited: 21 Feb 2023 17:40
Last modified: 18 Mar 2024 03:31
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