Abstract
Temperature prediction is a critical component of weather forecasting, impacting human life, safety, and property. Compared to traditional numerical weather prediction models, data-driven deep learning methods have more advantages in terms of computational time and resource consumption. However, existing deep learning methods also have inherent drawbacks, such as producing more ambiguous and diffused forecast results. To address these limitations, we introduce a novel model composed of a Spatiotemporal Perception Module (SPM) and an enhanced diffusion model. The SPM captures the long-term dependency information, serving as the generation condition for the diffusion model, and thereby endowing it with forecasting capabilities. We also introduce a new equilibrium loss function that balances the generation abilities of the diffusion model and the spatiotemporal information extraction capabilities of the SPM. Our model demonstrates superior performance on Weatherbench temperature prediction. It achieves a 13.3%.
Supported by the National Natural Science Foundation of China (Grant No.42075007) and the Open Project of Provincial Key Laboratory for Computer Information Processing Technology under Grant KJS2275, Soochow University.
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Fang, W., Yuan, Z., Xue, Q. (2023). SPM-Diffusion for Temperature Prediction. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14262. Springer, Cham. https://doi.org/10.1007/978-3-031-44201-8_31
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