26–30 Jun 2022
Europe/Berlin timezone

Comparing statistical and machine learning methods for time series forecasting in data-driven logistics - a simulation study

28 Jun 2022, 14:40


Other/special session/invited session INVITED Data Science for logistics 2


Ms Lena Schmid (TU Dortmund University)


With the development of an Industry 4.0, logistics systems will increasingly implement data-driven, automated decision-making processes. In this context, the quality of forecasts with multiple time-dependent factors is of particular importance.

In this talk, we compare time series and machine learning algorithms in terms of out-of-the-box forecasting performance on a broadset of simulated time series. To mimic different scenarios from warehousing such as storage in- and output we simulate various linear and non-linear time series and investigate the one-step forecast performance of these methods.

Keywords Forecasting, Machine Learning, Logistics

Primary author

Ms Lena Schmid (TU Dortmund University)


Mr Moritz Roidl (TU Dortmund University) Prof. Markus Pauly (TU Dortmund University)

Presentation materials

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