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Multi-Task Spatial-Temporal Transformer for Multi-Variable Meteorological Forecasting
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DOI：10.1109/TKDE.2024.3432599.png)
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Abstract
En 中文
This study delves into multi-variable meteorological spatial-temporal prediction, focusing on the simultaneous forecasting of key meteorological parameters such as temperature, wind speed, and atmospheric pressure. The core challenge of this task lies in identifying commonalities across different variables while capturing their unique features and the interactions among them. To address this, we propose a novel multi-task learning framework tailored for multi-variable meteorological forecasting. Our framework integrates a convolutional variable-specific visual representation module and a variable-interactive spatial-temporal inference module. The former extracts distinct variable information independently for each variable, while the latter employs a tri-level attention mechanism across space, time, and variables to uncover both commonalities and interactions among the variables. An adaptive multi-loss optimization strategy and a local information aggregation module are introduced to balance task optimization complexities and enhance representation stability. Comprehensive experiments across various meteorological prediction tasks confirm the effectiveness of our methods, showcasing superior performance over existing approaches.
Keywords:
Forecasting
Task analysis
Transformers
Predictive models
Multitasking
Atmospheric modeling
Convolutional neural networks
Meteorological forecasting
multi-task learning
spatial-temporal transformer
Journal
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6.7K
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