DEEP NEURAL FORECASTING MODELS FOR CLIMATE CHANGE ANALYSIS USING MULTIVARIATE TIME-SERIES DATA
Keywords:
Climate forecasting, multivariate time series, Temporal Fusion Transformer, Informer, N BEATS, ERA5, probabilistic forecasting, interpretability, graph neural networks.Abstract
Accurate and trustworthy multi-horizon predictions of several interrelated climate variables,including temperature, precipitation, and sea level pressure, are essential for assessing climate change. Inorder to assess the level of uncertainty
References
B. Lim, S. O. Arik, N. Loeff and T. Pfister, “Temporal Fusion Transformers for Interpretable Multi
Horizon Time Series Forecasting,” arXiv:1912.09363, Dec. 2019.
H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong and W. Zhang, “Informer: Beyond Efficient
Transformer for Long Sequence Time-Series Forecasting,” in Proceedings of AAAI, 2021.
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Published
2024-10-16
How to Cite
Dr. KISHOR KUMAR GAJULA,Dr. M. ANJAN KUMAR. (2024). DEEP NEURAL FORECASTING MODELS FOR CLIMATE CHANGE ANALYSIS USING MULTIVARIATE TIME-SERIES DATA . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 6356–6368. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3738
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