Revisiting Long-term Time Series Forecasting: An Investigation on Affine Mapping

Zhe Li, Shiyi Qi, Yiduo Li, Zenglin Xu

Primary Area: visualization or interpretation of learned representations

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Keywords: Time series forecasting, Affine mapping

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TL;DR: We provide theoretical and experimental explanations for the effect and limitation of linear models in long-term time series forecasting

Abstract: Long-term time series forecasting (LTSF) has gained significant attention in recent years. While there are various specialized designs for capturing temporal dependency, previous studies have demonstrated that a single linear layer can achieve competitive forecasting performance compared to other complex architectures. In this paper, we thoroughly investigate the intrinsic effectiveness of recent approaches and find that: 1) affine mapping in some LTSF models dominates forecasting performance across commonly utilized benchmarks; 2) affine mapping can effectively capture periodic patterns but encounter challenges when predicting non-periodic signals or time series with different periods across channels; and 3) using reversible normalization and increasing input horizon can significantly enhance the robustness of models. We provide theoretical and experimental explanations to support our findings and also discuss the limitations and future works. Our framework's code is available at \url{https://github.com/anonymous}.

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Submission Number: 1688