arXiv:2606.04135v1 Announce Type: new Abstract: Time series forecasting relies on historical patterns, but real-world series often exhibit non-stationarity and regime shifts that challenge fully parametric forecasters. Inspired by Retrieval-Augmented Generation (RAG), recent work augments forecasters by retrieving relevant historical segments and using them as external evidence at inference time. However, due to the intrinsic non-stationarity of real-world time series, a highly similar past segment does not necessarily imply a similar future, rendering similarity-only retrieval brittle and pro
The increasing complexity and non-stationarity of real-world time series data are pushing the limits of current forecasting models, leading researchers to explore hybrid approaches like RAG.
Improving time series forecasting accuracy, especially in dynamic environments, is critical for operational efficiency, risk management, and strategic planning across numerous industries.
This research introduces a more sophisticated approach to RAG-based forecasting by accounting for non-stationarity, potentially leading to more reliable and robust predictive models capable of handling complex data.
- · AI/ML researchers
- · Data scientists
- · Forecasting platform providers
- · Industries reliant on accurate predictions
- · Traditional fully parametric forecasters
- · Companies relying on simplistic forecasting models
More accurate predictive models become available for various applications.
Reduced errors in supply chain management, financial trading, energy demand prediction, and other time-critical operations.
Enhanced resilience and adaptability of systems to unpredictable market shifts and environmental changes, leading to significant economic advantages.
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Read at arXiv cs.LG