Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866914825860284416 |
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| author | Schmid, Lena Roidl, Moritz Pauly, Markus |
| author_facet | Schmid, Lena Roidl, Moritz Pauly, Markus |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_07139 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study Schmid, Lena Roidl, Moritz Pauly, Markus Machine Learning 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. |
| title | Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2303.07139 |