AI RESEARCH
PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting
arXiv CS.LG
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ArXi:2605.16793v1 Announce Type: new Time series forecasting under non-stationarity faces a fundamental tension between capturing stable representations and adapting to distribution shifts. Existing methods implicitly rely on static historical assumptions, leading to a critical failure mode we term Phase Amnesia, where models become blind to the evolving global context. To resolve this, we formalize non-stationary dynamics through three physical hypotheses: wold decomposition, dynamical phase evolution, and heteroscedastic manifold generation.