ENBIS-22 Trondheim Conference

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

28 Jun 2022, 14:40

30m

EL2

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

Speaker

Ms Lena Schmid (TU Dortmund University)

Description

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.