TSPP: A Unified Benchmarking Tool for Time-series Forecasting
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
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| _version_ | 1866917560978505728 |
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| author | Bączek, Jan Zhylko, Dmytro Titericz, Gilberto Darabi, Sajad Puget, Jean-Francois Putterman, Izzy Majchrowski, Dawid Gupta, Anmol Kranen, Kyle Morkisz, Pawel |
| author_facet | Bączek, Jan Zhylko, Dmytro Titericz, Gilberto Darabi, Sajad Puget, Jean-Francois Putterman, Izzy Majchrowski, Dawid Gupta, Anmol Kranen, Kyle Morkisz, Pawel |
| contents | While machine learning has witnessed significant advancements, the emphasis has largely been on data acquisition and model creation. However, achieving a comprehensive assessment of machine learning solutions in real-world settings necessitates standardization throughout the entire pipeline. This need is particularly acute in time series forecasting, where diverse settings impede meaningful comparisons between various methods. To bridge this gap, we propose a unified benchmarking framework that exposes the crucial modelling and machine learning decisions involved in developing time series forecasting models. This framework fosters seamless integration of models and datasets, aiding both practitioners and researchers in their development efforts. We benchmark recently proposed models within this framework, demonstrating that carefully implemented deep learning models with minimal effort can rival gradient-boosting decision trees requiring extensive feature engineering and expert knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17100 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | TSPP: A Unified Benchmarking Tool for Time-series Forecasting Bączek, Jan Zhylko, Dmytro Titericz, Gilberto Darabi, Sajad Puget, Jean-Francois Putterman, Izzy Majchrowski, Dawid Gupta, Anmol Kranen, Kyle Morkisz, Pawel Machine Learning While machine learning has witnessed significant advancements, the emphasis has largely been on data acquisition and model creation. However, achieving a comprehensive assessment of machine learning solutions in real-world settings necessitates standardization throughout the entire pipeline. This need is particularly acute in time series forecasting, where diverse settings impede meaningful comparisons between various methods. To bridge this gap, we propose a unified benchmarking framework that exposes the crucial modelling and machine learning decisions involved in developing time series forecasting models. This framework fosters seamless integration of models and datasets, aiding both practitioners and researchers in their development efforts. We benchmark recently proposed models within this framework, demonstrating that carefully implemented deep learning models with minimal effort can rival gradient-boosting decision trees requiring extensive feature engineering and expert knowledge. |
| title | TSPP: A Unified Benchmarking Tool for Time-series Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2312.17100 |