Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 104 references that are in the map. First posted on arXiv 13 Mar 2023.
Builds on
7 of 104 references.
References · 7
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- 2020Travel Time Prediction using Tree-Based Ensemblescited by 5
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Citing papers per year: 2026: 2.
2 papers.