Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

• URL: http://arxiv.org/abs/2106.13008v1
• Date: Thu, 24 Jun 2021 13:43:43 GMT
• Title: Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
• Authors: Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long
• Abstract summary: Autoformer is a novel decomposition architecture with an Auto-Correlation mechanism. In long-term forecasting, Autoformer yields state-of-the-art accuracy, with a relative improvement on six benchmarks.
• Score: 68.86835407617778
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 \textit{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. Towards these challenges, we propose Autoformer as a novel decomposition architecture with an Auto-Correlation mechanism. We go beyond 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 complex time series. Further, inspired by the stochastic process theory, we design the Auto-Correlation mechanism based on the series periodicity, which conducts the dependencies discovery and representation aggregation at the sub-series level. Auto-Correlation outperforms self-attention in both efficiency and accuracy. In long-term forecasting, Autoformer yields state-of-the-art accuracy, with a 38% relative improvement on six benchmarks, covering five practical applications: energy, traffic, economics, weather and disease.

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