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

• URL: http://arxiv.org/abs/2410.11674v1
• Date: Tue, 15 Oct 2024 15:08:57 GMT
• Title: LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
• Authors: Md Kowsher, Md. Shohanur Islam Sobuj, Nusrat Jahan Prottasha, E. Alejandro Alanis, Ozlem Ozmen Garibay, Niloofar Yousefi,
• Abstract summary: LLM-Mixer is a framework that improves forecasting accuracy through the combination of multiscale time-series decomposition with pre-trained LLMs. It captures both short-term fluctuations and long-term trends by decomposing the data into multiple temporal resolutions.
• Score: 0.08795040582681389
• License:
• 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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