Abstract
Time series forecasting is a widely used statistical technique that use past data to predict future values of variables. Its applications span across various fields, including finance, economics, and marketing. Multivariate time series forecasting, which involves two or more variables, is more complex than univariate time series forecasting and to address this complexity, neural networks are commonly used. However, the selection of an appropriate forecasting method is contingent upon the specific characteristics of the data. This paper proposes a new methodology that addresses such an issue and applies it to climate forecasting. The use of time series forecasting in climate forecasting has the potential to enhance our understanding of climate change and its impacts on various aspects of human life.
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Acknowledgements
This research is supported by the project Future Artificial Intelligence Research (FAIR) - PNRR MUR Cod. PE0000013 - CUP: E63C22001940006.
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Zito, F., Cutello, V., Pavone, M. (2023). Deep Learning and Metaheuristic for Multivariate Time-Series Forecasting. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-031-42529-5_24
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DOI: https://doi.org/10.1007/978-3-031-42529-5_24
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