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 42nd International Conference on Machine Learning , 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.