Retrieval-augmented forecasting outperforms long-context scaling in time series models on ETTh1, with an inverse scaling law where error rises as context length increases.
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics , pages =
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Retrieval Mechanisms Surpass Long-Context Scaling in Time Series Forecasting
Retrieval-augmented forecasting outperforms long-context scaling in time series models on ETTh1, with an inverse scaling law where error rises as context length increases.