mlsanityData to 14 Oct 2026

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

arXiv 2410.11674 ↗First posted 15 Oct 2024cs.LG cs.CLcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 23 of its 51 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

23 of 51 references.

References · 23

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11 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 7.

8 papers.

  1. 2026Unlocking the Value of Text: Event-Driven Reasoning and Multi-Level Alignment for Time Series Forecastingcited by 5
  2. 2025TsLLM: Augmenting LLMs for General Time Series Understanding and Predictioncited by 4
  3. 2026Empowering Time Series Analysis with Large-Scale Multimodal Pretrainingcited by 3
  4. 2026Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interactioncited by 3
  5. 2026ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detectioncited by 1
  6. 2026QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalitiescited by 1
  7. 2026Predicting the Unpredictable: LLM-powered Long-term Chaotic Time Series Forecasting under Short-term Observationscited by 0
  8. 2026LEARN-TS: LLM-Enhanced Alignment and Reconstruction with Normality Guidance for Multivariate Time-Series Anomaly Detectioncited by 0