LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
This addresses forecasting challenges for domains with complex temporal patterns, though it appears incremental as a hybrid approach.
The paper tackles time series forecasting by combining multiscale decomposition with pre-trained LLMs, achieving competitive performance that outperforms recent state-of-the-art models across various forecasting horizons.
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.