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arXiv · 2410.11674

LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting

Md Kowsher·Md. Shohanur Islam Sobuj·Nusrat Jahan Prottasha·E. Alejandro Alanis·Ozlem Ozmen Garibay·Niloofar Yousefi
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Abstract

Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves forecasting accuracy through the combination of multiscale time-series decomposition with pre-trained LLMs (Large Language Models). LLM-Mixer captures both short-term fluctuations and long-term trends by decomposing the data into multiple temporal resolutions and processing them with a frozen LLM, guided by a textual prompt specifically designed for time-series data. Extensive experiments conducted on multivariate and univariate datasets demonstrate that LLM-Mixer achieves competitive performance, outperforming recent state-of-the-art models across various forecasting horizons. This work highlights the potential of combining multiscale analysis and LLMs for effective and scalable time-series forecasting.

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Md Kowsher, Md. Shohanur Islam Sobuj, Nusrat Jahan Prottasha, E. Alejandro Alanis, Ozlem Ozmen Garibay, Niloofar Yousefi. 2024-10-15. LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting. https://arxiv.org/abs/2410.11674

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