Abstract: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. These principles inspire PULSE, a physics-informed, plug-and-play framework adopting a Disentangle--Evolve--Simulate design philosophy. Specifically, PULSE utilizes phase-anchored disentanglement to resolve optimization interference caused by dominant trends, employs a Phase Router to actively generate future trajectories, and introduces Statistic-Aware Mixup (SAM) to ensure robustness against out-of-distribution volatility. Empirically, PULSE enables a simple MLP backbone to achieve state-of-the-art or highly competitive performance across 12 real-world benchmarks. This validates that a correct physics-informed inductive bias is far more critical than raw architectural complexity for non-stationary forecasting. The code is available at: this https URL.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.16793 [cs.LG] |
| (or arXiv:2605.16793v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16793 arXiv-issued DOI via DataCite (pending registration) |
From: Yangyou Liu [view email]
[v1]
Sat, 16 May 2026 03:54:18 UTC (1,184 KB)
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