Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study
This work addresses forecasting accuracy for logistics planning, but it is incremental as it focuses on a simulation-based comparison without new methods.
The study compared statistical and machine learning methods for time series forecasting in logistics, finding that they evaluated out-of-the-box performance on simulated linear and non-linear time series.
Many planning and decision activities in logistics and supply chain management are based on forecasts of multiple time dependent factors. Therefore, the quality of planning depends on the quality of the forecasts. We compare various forecasting methods in terms of out of the box forecasting performance on a broad set of simulated time series. We simulate various linear and non-linear time series and look at the one step forecast performance of statistical learning methods.