LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866918042421690368 |
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| author | Kowsher, Md Sobuj, Md. Shohanur Islam Prottasha, Nusrat Jahan Alanis, E. Alejandro Garibay, Ozlem Ozmen Yousefi, Niloofar |
| author_facet | Kowsher, Md Sobuj, Md. Shohanur Islam Prottasha, Nusrat Jahan Alanis, E. Alejandro Garibay, Ozlem Ozmen Yousefi, Niloofar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11674 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting Kowsher, Md Sobuj, Md. Shohanur Islam Prottasha, Nusrat Jahan Alanis, E. Alejandro Garibay, Ozlem Ozmen Yousefi, Niloofar Machine Learning Computation and Language 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. |
| title | LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2410.11674 |