Autoformer Overview The Autoformer model was proposed in Autoformer: Decomposition Transformers with Auto Correlation for Long Term Series Forecasting by Haixu Wu, Jiehui Xu, Jianmin Wang and Mingsheng Long. The abstract from the paper is the following: Extending the forecasting time is a critical demand for real applications, such as extreme weather early warning and long term energy consumption planning. This paper studies the long term forecasting problem of time series. Prior Transformer based models adopt various self attention mechanisms to discover the long range dependencies. However, intricate temporal patterns of the long term future prohibit the model from finding reliable dependencies. Also, Transformers have to adopt the sparse versions of point wise self attentions for long series efficiency, resulting in the information utilization bottleneck. Going beyond Transformers, we design Autoformer as a novel decomposition architecture with an Auto Correlation mechanism. We break with the pre processing convention of series decomposition and renovate it as a basic inner block of deep models. This design empowers Autoformer with progressive decomposition capacities for comple…
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