TENT: Tensorized Encoder Transformer for Temperature Forecasting
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Reliable weather forecasting is of great importance in science, business and society. The best performing data-driven models for weather prediction tasks rely on recurrent or convolutional neural networks, where some of which incorporate attention mechanisms. In this work, we introduce a new model based on the Transformer architecture for weather forecasting. The proposed Tensorial Encoder Transformer (TENT) model is equipped with tensorial attention and thus it exploits the spatiotemporal structure of weather data by processing it in multidimensional tensorial format. We show that compared to the encoder part of the original transformer and 3D convolutional neural networks, the proposed TENT model can better model the underlying complex pattern of weather data for the studied temperature prediction task. Experiments on two real-life weather datasets are performed. The datasets consist of historical measurements from USA, Canada and European cities. The first dataset contains hourly measurements of weather attributes for 30 cities in USA and Canada from October 2012 to November 2017. The second dataset contains daily measurements of weather attributes of 18 cities across Europe from May 2005 to April 2020. We use attention scores calculated from our attention mechanism to shed light on the decision-making process of our model and have insight knowledge on the most important cities for the task.
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