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Enhancing Time Series Forecasting via Multi-Level Text Alignment with LLMs
- The adaptation of large language models (LLMs) to time series forecasting poses unique challenges.
- A multi-level text alignment framework for time series forecasting using LLMs is proposed.
- The method decomposes time series into trend, seasonal, and residual components.
- Experiments show that the proposed method outperforms state-of-the-art models in accuracy and interpretability.
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