The proposed framework decomposes retrieval-augmented representations into invariant and dynamic components to improve robustness in zero-shot time series forecasting under distribution shifts.
Deep learning for energy time-series analysis and forecasting.arXiv preprint arXiv:2306.09129,
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Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting
The proposed framework decomposes retrieval-augmented representations into invariant and dynamic components to improve robustness in zero-shot time series forecasting under distribution shifts.