FLAIRR-TS - Forecasting LLM-Agents with Iterative Refinement and Retrieval for Time Series (2025.findings-emnlp)
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| Challenge: | Effective time series forecasting with large language models often relies on extensive pre-processing and fine-tuning. |
| Approach: | a new time series prompt optimization framework is developed to optimize time series forecasts. |
| Outcome: | The proposed framework improves forecasting over static prompting and retrieval-augmented baselines. |
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