Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics—A Simulation Study
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 different state-of-the-art forecasting methods in terms of forecasting perf...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/1/25 |
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| author | Lena Schmid Moritz Roidl Alice Kirchheim Markus Pauly |
| author_facet | Lena Schmid Moritz Roidl Alice Kirchheim Markus Pauly |
| author_sort | Lena Schmid |
| collection | DOAJ |
| description | 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 different state-of-the-art forecasting methods in terms of forecasting performance. Differently from most existing research in logistics, we do not perform this in a case-dependent way but consider a broad set of simulated time series to give more general recommendations. We therefore simulate various linear and nonlinear time series that reflect different situations. Our simulation results showed that the machine learning methods, especially Random Forests, performed particularly well in complex scenarios, with the differentiated time series training significantly improving the robustness of the model. In addition, the time series approaches proved to be competitive in low noise scenarios. |
| format | Article |
| id | doaj-art-769d0f9e632041aba8d2f837fc8c429e |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-769d0f9e632041aba8d2f837fc8c429e2025-01-24T13:31:43ZengMDPI AGEntropy1099-43002024-12-012712510.3390/e27010025Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics—A Simulation StudyLena Schmid0Moritz Roidl1Alice Kirchheim2Markus Pauly3Department of Statistics, TU Dortmund University, 44227 Dortmund, GermanyChair of Material Handling and Warehousing, TU Dortmund University, 44227 Dortmund, GermanyChair of Material Handling and Warehousing, TU Dortmund University, 44227 Dortmund, GermanyDepartment of Statistics, TU Dortmund University, 44227 Dortmund, GermanyMany 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 different state-of-the-art forecasting methods in terms of forecasting performance. Differently from most existing research in logistics, we do not perform this in a case-dependent way but consider a broad set of simulated time series to give more general recommendations. We therefore simulate various linear and nonlinear time series that reflect different situations. Our simulation results showed that the machine learning methods, especially Random Forests, performed particularly well in complex scenarios, with the differentiated time series training significantly improving the robustness of the model. In addition, the time series approaches proved to be competitive in low noise scenarios.https://www.mdpi.com/1099-4300/27/1/25machine learningtime seriesforecastingsimulation study |
| spellingShingle | Lena Schmid Moritz Roidl Alice Kirchheim Markus Pauly Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics—A Simulation Study Entropy machine learning time series forecasting simulation study |
| title | Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics—A Simulation Study |
| title_full | Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics—A Simulation Study |
| title_fullStr | Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics—A Simulation Study |
| title_full_unstemmed | Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics—A Simulation Study |
| title_short | Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics—A Simulation Study |
| title_sort | comparing statistical and machine learning methods for time series forecasting in data driven logistics a simulation study |
| topic | machine learning time series forecasting simulation study |
| url | https://www.mdpi.com/1099-4300/27/1/25 |
| work_keys_str_mv | AT lenaschmid comparingstatisticalandmachinelearningmethodsfortimeseriesforecastingindatadrivenlogisticsasimulationstudy AT moritzroidl comparingstatisticalandmachinelearningmethodsfortimeseriesforecastingindatadrivenlogisticsasimulationstudy AT alicekirchheim comparingstatisticalandmachinelearningmethodsfortimeseriesforecastingindatadrivenlogisticsasimulationstudy AT markuspauly comparingstatisticalandmachinelearningmethodsfortimeseriesforecastingindatadrivenlogisticsasimulationstudy |