TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change
Climate change presents significant challenges due to the increasing frequency and intensity of extreme weather events. Mexico, with its diverse climate and geographic position, is particularly vulnerable, underscoring the need for robust strategies to predict atmospheric variables. This work presen...
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MDPI AG
2025-04-01
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| Series: | Mathematical and Computational Applications |
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| Online Access: | https://www.mdpi.com/2297-8747/30/3/46 |
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| author | Juan Frausto Solís Erick Estrada-Patiño Mirna Ponce Flores Juan Paulo Sánchez-Hernández Guadalupe Castilla-Valdez Javier González-Barbosa |
| author_facet | Juan Frausto Solís Erick Estrada-Patiño Mirna Ponce Flores Juan Paulo Sánchez-Hernández Guadalupe Castilla-Valdez Javier González-Barbosa |
| author_sort | Juan Frausto Solís |
| collection | DOAJ |
| description | Climate change presents significant challenges due to the increasing frequency and intensity of extreme weather events. Mexico, with its diverse climate and geographic position, is particularly vulnerable, underscoring the need for robust strategies to predict atmospheric variables. This work presents TAE Predict (Time series Analysis and Ensemble-based Prediction with relevant feature selection) based on relevant feature selection and ensemble models of machine learning. Dimensionality in multivariate time series is reduced through Principal Component Analysis, ensuring interpretability and efficiency. Additionally, data remediation techniques improve data set quality. The ensemble combines Long Short-Term Memory neural networks, Random Forest regression, and Support Vector Machines, optimizing their contributions using heuristic algorithms such as Particle Swarm Optimization. Experimental results from meteorological time series in key Mexican cities demonstrate that the proposed strategy outperforms individual models in accuracy and robustness. This methodology provides a replicable framework for climate variable forecasting, delivering analytical tools that support decision-making in critical sectors, such as agriculture and water resource management. The findings highlight the potential of integrating modern techniques to address complex, high-dimensional problems. By combining advanced prediction models and feature selection strategies, this study advances the reliability of climate forecasts and contributes to the development of effective adaptation and mitigation measures in response to climate change challenges. |
| format | Article |
| id | doaj-art-ef6b8f3198c94fccb7b22b80f17efeca |
| institution | Kabale University |
| issn | 1300-686X 2297-8747 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematical and Computational Applications |
| spelling | doaj-art-ef6b8f3198c94fccb7b22b80f17efeca2025-08-20T03:27:24ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472025-04-013034610.3390/mca30030046TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate ChangeJuan Frausto Solís0Erick Estrada-Patiño1Mirna Ponce Flores2Juan Paulo Sánchez-Hernández3Guadalupe Castilla-Valdez4Javier González-Barbosa5Graduate Program Division, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero 89440, MexicoGraduate Program Division, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero 89440, MexicoGraduate Program Division, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero 89440, MexicoDirección de Informático, Electrónica y Telecomunicaciones, Universidad Politécnica del Estado de Morelos, Boulevard Cuauhnáhuac 566, Jiutepec 62574, MexicoGraduate Program Division, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero 89440, MexicoGraduate Program Division, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero 89440, MexicoClimate change presents significant challenges due to the increasing frequency and intensity of extreme weather events. Mexico, with its diverse climate and geographic position, is particularly vulnerable, underscoring the need for robust strategies to predict atmospheric variables. This work presents TAE Predict (Time series Analysis and Ensemble-based Prediction with relevant feature selection) based on relevant feature selection and ensemble models of machine learning. Dimensionality in multivariate time series is reduced through Principal Component Analysis, ensuring interpretability and efficiency. Additionally, data remediation techniques improve data set quality. The ensemble combines Long Short-Term Memory neural networks, Random Forest regression, and Support Vector Machines, optimizing their contributions using heuristic algorithms such as Particle Swarm Optimization. Experimental results from meteorological time series in key Mexican cities demonstrate that the proposed strategy outperforms individual models in accuracy and robustness. This methodology provides a replicable framework for climate variable forecasting, delivering analytical tools that support decision-making in critical sectors, such as agriculture and water resource management. The findings highlight the potential of integrating modern techniques to address complex, high-dimensional problems. By combining advanced prediction models and feature selection strategies, this study advances the reliability of climate forecasts and contributes to the development of effective adaptation and mitigation measures in response to climate change challenges.https://www.mdpi.com/2297-8747/30/3/46climate changemultivariate time seriesdeep learningprincipal component analysisensemble methodsparticle swarm optimization |
| spellingShingle | Juan Frausto Solís Erick Estrada-Patiño Mirna Ponce Flores Juan Paulo Sánchez-Hernández Guadalupe Castilla-Valdez Javier González-Barbosa TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change Mathematical and Computational Applications climate change multivariate time series deep learning principal component analysis ensemble methods particle swarm optimization |
| title | TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change |
| title_full | TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change |
| title_fullStr | TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change |
| title_full_unstemmed | TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change |
| title_short | TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change |
| title_sort | tae predict an ensemble methodology for multivariate time series forecasting of climate variables in the context of climate change |
| topic | climate change multivariate time series deep learning principal component analysis ensemble methods particle swarm optimization |
| url | https://www.mdpi.com/2297-8747/30/3/46 |
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