An AutoML Framework using AutoGluonTS for Forecasting Seasonal Extreme Temperatures
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911168393641984 |
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| author | Rodríguez-Bocca, Pablo Pereira, Guillermo Kiedanski, Diego Collazo, Soledad Basterrech, Sebastián Rubino, Gerardo |
| author_facet | Rodríguez-Bocca, Pablo Pereira, Guillermo Kiedanski, Diego Collazo, Soledad Basterrech, Sebastián Rubino, Gerardo |
| contents | In recent years, great progress has been made in the field of forecasting meteorological variables. Recently, deep learning architectures have made a major breakthrough in forecasting the daily average temperature over a ten-day horizon. However, advances in forecasting events related to the maximum temperature over short horizons remain a challenge for the community. A problem that is even more complex consists in making predictions of the maximum daily temperatures in the short, medium, and long term. In this work, we focus on forecasting events related to the maximum daily temperature over medium-term periods (90 days). Therefore, instead of addressing the problem from a meteorological point of view, this article tackles it from a climatological point of view. Due to the complexity of this problem, a common approach is to frame the study as a temporal classification problem with the classes: maximum temperature "above normal", "normal" or "below normal". From a practical point of view, we created a large historical dataset (from 1981 to 2018) collecting information from weather stations located in South America. In addition, we also integrated exogenous information from the Pacific, Atlantic, and Indian Ocean basins. We applied the AutoGluonTS platform to solve the above-mentioned problem. This AutoML tool shows competitive forecasting performance with respect to large operational platforms dedicated to tackling this climatological problem; but with a "relatively" low computational cost in terms of time and resources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17734 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An AutoML Framework using AutoGluonTS for Forecasting Seasonal Extreme Temperatures Rodríguez-Bocca, Pablo Pereira, Guillermo Kiedanski, Diego Collazo, Soledad Basterrech, Sebastián Rubino, Gerardo Machine Learning Computational Engineering, Finance, and Science 62M10, 68T05, 86A08, 62P12 I.2.6; I.5.1; G.3; J.2 In recent years, great progress has been made in the field of forecasting meteorological variables. Recently, deep learning architectures have made a major breakthrough in forecasting the daily average temperature over a ten-day horizon. However, advances in forecasting events related to the maximum temperature over short horizons remain a challenge for the community. A problem that is even more complex consists in making predictions of the maximum daily temperatures in the short, medium, and long term. In this work, we focus on forecasting events related to the maximum daily temperature over medium-term periods (90 days). Therefore, instead of addressing the problem from a meteorological point of view, this article tackles it from a climatological point of view. Due to the complexity of this problem, a common approach is to frame the study as a temporal classification problem with the classes: maximum temperature "above normal", "normal" or "below normal". From a practical point of view, we created a large historical dataset (from 1981 to 2018) collecting information from weather stations located in South America. In addition, we also integrated exogenous information from the Pacific, Atlantic, and Indian Ocean basins. We applied the AutoGluonTS platform to solve the above-mentioned problem. This AutoML tool shows competitive forecasting performance with respect to large operational platforms dedicated to tackling this climatological problem; but with a "relatively" low computational cost in terms of time and resources. |
| title | An AutoML Framework using AutoGluonTS for Forecasting Seasonal Extreme Temperatures |
| topic | Machine Learning Computational Engineering, Finance, and Science 62M10, 68T05, 86A08, 62P12 I.2.6; I.5.1; G.3; J.2 |
| url | https://arxiv.org/abs/2509.17734 |