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

Extending the forecasting time is a critical demand for real applications,\nsuch as extreme weather early warning and long-term energy consumption\nplanning. In long-term\nforecasting, Autoformer yields advanced accuracy, with a 38% relative\nimprovement on six benchmarks, covering five practical applications: energy,\ntraffic, economics, weather and disease.

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• 1 This paper studies the long-term forecasting problem of time series.\nPrior Transformer-based models adopt various self-attention mechanisms to\ndiscover the long-range dependencies.
• 2 However, intricate temporal patterns of\nthe long-term future prohibit the model from finding reliable dependencies.\nAlso, Transformers have to adopt the sparse versions of point-wise\nself-attentions for long series efficiency, resulting in the information\nutilization bottleneck.
• 3 Going beyond Transformers, we design Autoformer as a\nnovel decomposition architecture with an Auto-Correlation mechanism.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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