arXiv:2605.16793v2 Announce Type: replace 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 p
The increasing prevalence of non-stationary time series data in real-world applications (e.g., financial markets, climate modeling, complex systems) necessitates more adaptive and robust forecasting methods that address dynamic changes over time.
Improved models for non-stationary time series forecasting can lead to more accurate predictions across various critical domains, enhancing decision-making and risk mitigation in environments characterized by constant change.
Traditional forecasting models, which often assume static historical properties, will be challenged by new frameworks like PULSE that explicitly tackle phase evolution and distribution shifts, leading to more resilient predictive analytics.
- · AI/ML researchers
- · Financial services
- · Climate modeling institutions
- · Logistics and supply chain management
- · Companies relying on static forecasting models
- · Legacy time series analysis software
More accurate predictive models become available for complex, dynamic systems.
Industries reliant on forecasting may see increased efficiency and reduced errors in planning and operations.
The ability to anticipate and adapt to 'phase amnesia' could become a competitive advantage, driving new research and development in adaptive AI systems.
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