Summary of Llm-mixer: Multiscale Mixing in Llms For Time Series Forecasting, by Md Kowsher et al.
LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
by Md Kowsher, Md. Shohanur Islam Sobuj, Nusrat Jahan Prottasha, E. Alejandro Alanis, Ozlem Ozmen Garibay, Niloofar Yousefi
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this study, researchers introduce LLM-Mixer, a framework that enhances time series forecasting by integrating multiscale temporal pattern decomposition with pre-trained Large Language Models (LLMs). The proposed method breaks down data into various time resolutions, processing each scale using a frozen LLM guided by a tailored textual prompt for time-series analysis. Experiments on multivariate and univariate datasets show that LLM-Mixer yields competitive results, outperforming state-of-the-art models across diverse forecasting horizons. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special computers to make better predictions about what will happen in the future based on patterns they find in old data. It works by looking at different parts of the data and using a special kind of computer program called a language model to help figure out what might happen next. The researchers tested their idea on lots of different kinds of data and found that it was good at making predictions. |
Keywords
» Artificial intelligence » Language model » Prompt » Time series
