代表(政治)
降水
计算机科学
特征(语言学)
人工智能
特征学习
天气预报
期限(时间)
机器学习
自编码
系列(地层学)
气象学
深度学习
语言学
地理
地质学
哲学
物理
量子力学
政治
政治学
法学
古生物学
作者
Guohan Wu,Chi‐Hua Chen
标识
DOI:10.1109/icce56470.2023.10043429
摘要
Short-term weather forecasting is of great significance to people's lives, especially in terms of transportation. Since daily precipitation can be regarded as a nonlinear and non-stationary time series, it is difficult to predict it. Based on the contrastive learning method, A multimodal representation method for forecasting daily precipitation is proposed. Specifically, an encoder is used to convert the original weather data into feature representation, then it is optimized through contrastive learning, and the optimized features are used for prediction.
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