Weather Forecasting Using Ensemble of Spatial-Temporal Attention Network and Multi-Layer Perceptron

摘要

Weather forecasting is a challenging task, which is especially suited for artificial intelligence due to the large amount of data involved. This paper proposed an end-to-end hybrid regression model, calledEnsemble ofSpatial-TemporalAttentionNetwork andMulti-LayerPerceptron (E-STAN-MLP), to forecast surface temperature, humidity, wind speed, and wind direction at 24 automatic weather stations in Beijing. Combining the data from historical observations with the data from the numerical weather prediction (NWP) system, our proposed model give better results than the NWP system or previously reported algorithms. Our E-STAN-MLP model consists of two parts. One is to use the spatial-temporal attention based recurrent neural network to model the time series of meteorological elements. The other is a simple but efficient multi-layer perceptron architecture forecasts the regression value while ignoring time dependence. Results at each time stamp are integrated together using a step-wise fusion strategy. Moreover, we use a joint loss step integrating both the regression loss function and the classification loss function to simultaneously forecast the wind speed and direction. Experiments demonstrate that our proposed E-STAN-MLP model achieves state-of-the-art results in weather forecasting.