Retrieval for Time-Series: How Looking Back Improves Forecasts

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2026-01-08 15:01:02 -

Retrieval-augmented approaches aren't just for LLMs anymore. This piece explores how pulling in similar historical patterns can help time-series models handle those tricky edge cases — think market crashes or rare weather events — that even models like Chronos stumble on. A solid technical read if you're working with forecasting.

Retrieval-augmented approaches aren't just for LLMs anymore. This piece explores how pulling in similar historical patterns can help time-series models handle those tricky edge cases — think market crashes or rare weather events — that even models like Chronos stumble on. 📊 A solid technical read if you're working with forecasting.

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Retrieval for Time-Series: How Looking Back Improves Forecasts

Why Retrieval Helps in Time Series Forecasting We all know how it goes: Time-series data is tricky. Traditional forecasting models are unprepared for incidents like sudden market crashes, black swan events, or rare weather patterns. Even large fancy models like Chronos sometimes struggle because they haven’t dealt with that kind of pattern before. We can […] The post Retrieval for Time-Series: How Looking Back Improves Forecasts appeared first on Towards Data Science.

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