A spatial–temporal graph attention network approach for air temperature forecasting
Abstract
Air temperature prediction is a significant task for researchers and forecasters in the field of meteorology. In this paper, we present an innovative, deep spatial–temporal learning air temperature forecasting framework based on graph attention network and gated recurrent unit. Particularly, historical observations containing multiple environmental variables at different stations are constructed as graph signals. The original stations’...
Paper Details
Title
A spatial–temporal graph attention network approach for air temperature forecasting
Published Date
Dec 1, 2021
Journal
Volume
113
Pages
107888 - 107888
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