Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting
arXiv:2605.24911v1 Announce Type: new Abstract: Time series foundation models (TSFMs) have recently achieved strong zero-shot forecasting performance through large-scale pretraining and retrieval-augmented prediction. However, our empirical analysis reveals a non-trivial limitation of retrieval-based forecasting: retrieval tends to induce more oscillatory predictions, improving performance on highly fluctuating series while degrading accuracy on smoother, trend-dominated ones. This suggests that retrieved information may be fused into prediction without explicitly distinguishing stable tempora
The proliferation of time series foundation models and retrieval-augmented prediction necessitates deeper understanding of their limitations and optimization strategies, particularly as AI capabilities advance.
Improving time series forecasting accuracy, especially in distinguishing between highly fluctuating and trend-dominated data, is crucial for various sectors from finance to logistics and energy management.
This research outlines a method to enhance retrieval-based forecasting by explicitly separating invariant and dynamic components, leading to more robust and accurate predictions across diverse time series data.
- · AI developers
- · Analytics software providers
- · Industries relying on forecasting (finance, logistics, energy)
- · Legacy forecasting models
- · Companies with undifferentiated time series AI solutions
More accurate and reliable time series forecasts become achievable across various applications.
Improved predictive capabilities lead to optimized resource allocation and reduced operational risks in businesses.
The enhanced foundational capabilities of time series AI models could accelerate automation and intelligent decision-making across critical infrastructure and economic sectors.
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Read at arXiv cs.LG