  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 four week
course that the author has taught for seven years to students on operations
research, management science, analytics, and statistics one-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 are made available (see 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.
