AI RESEARCH
TimeAPN: Adaptive Amplitude-Phase Non-Stationarity Normalization for Time Series Forecasting
arXiv CS.LG
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ArXi:2603.17436v1 Announce Type: new Non-stationarity is a fundamental challenge in multivariate long-term time series forecasting, often manifested as rapid changes in amplitude and phase. These variations lead to severe distribution shifts and consequently degrade predictive performance. Existing normalization-based methods primarily rely on first- and second-order statistics, implicitly assuming that distributions evolve smoothly and overlooking fine-grained temporal dynamics.