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.

Related papers

• Position: Empowering Time Series Reasoning with Multimodal LLMs [49.73647759532127]
  We argue that multimodal language models (MLLMs) can enable more powerful and flexible reasoning for time series analysis. We call on researchers and practitioners to leverage this potential by developing strategies that prioritize trust, interpretability, and robust reasoning in MLLMs.
  arXiv  Detail & Related papers  (2025-02-03T16:10:48Z)
• Large Language Models are Few-shot Multivariate Time Series Classifiers [23.045734479292356]
  Large Language Models (LLMs) have been extensively applied in time series analysis. Yet, their utility in the few-shot classification (i.e., a crucial training scenario) is underexplored. We aim to leverage the extensive pre-trained knowledge in LLMs to overcome the data scarcity problem.
  arXiv  Detail & Related papers  (2025-01-30T03:59:59Z)
• LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting [9.132953776171808]
  This paper introduces an LLM-enhanced mixture of linear experts for precise and efficient time series forecasting. The use of a mixture of linear experts is efficient due to its simplicity, while the multimodal fusion mechanism adaptively combines multiple linear experts. Our experimental results show that the proposed LeMoLE model presents lower prediction errors and higher computational efficiency than existing LLM models.
  arXiv  Detail & Related papers  (2024-11-24T12:40:50Z)
• SoupLM: Model Integration in Large Language and Multi-Modal Models [51.12227693121004]
  Training large language models (LLMs) requires significant computing resources. Existing publicly available LLMs are typically pre-trained on diverse, privately curated datasets spanning various tasks.
  arXiv  Detail & Related papers  (2024-07-11T05:38:15Z)
• MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
  We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
  arXiv  Detail & Related papers  (2024-05-30T03:32:44Z)
• TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting [19.88184356154215]
  Time series forecasting is widely used in applications, such as traffic planning and weather forecasting. TimeMixer is able to achieve consistent state-of-the-art performances in both long-term and short-term forecasting tasks.
  arXiv  Detail & Related papers  (2024-05-23T14:27:07Z)
• Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities [46.02234423159257]
  Large language models (LLMs) have been applied in many fields and have developed rapidly in recent years. Recent works treat large language models as emphzero-shot time series reasoners without further fine-tuning. Our study shows that LLMs perform well in predicting time series with clear patterns and trends, but face challenges with datasets lacking periodicity.
  arXiv  Detail & Related papers  (2024-02-16T17:15:28Z)
• AutoTimes: Autoregressive Time Series Forecasters via Large Language Models [67.83502953961505]
  AutoTimes projects time series into the embedding space of language tokens and autoregressively generates future predictions with arbitrary lengths. We formulate time series as prompts, extending the context for prediction beyond the lookback window. AutoTimes achieves state-of-the-art with 0.1% trainable parameters and over $5times$ training/inference speedup.
  arXiv  Detail & Related papers  (2024-02-04T06:59:21Z)
• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models [110.20279343734548]
  Time series forecasting holds significant importance in many real-world dynamic systems. We present Time-LLM, a reprogramming framework to repurpose large language models for time series forecasting. Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models.
  arXiv  Detail & Related papers  (2023-10-03T01:31:25Z)
• LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters [11.796765525301051]
  We propose a framework for time-series forecasting with pre-trained Large Language Models (LLMs) LLM4TS consists of a two-stage fine-tuning strategy to align LLMs with the nuances of time-series data, and the forecasting fine-tuning stage for downstream time-series forecasting tasks. Our framework features a novel two-level aggregation method that integrates multi-scale temporal data within pre-trained LLMs, enhancing their ability to interpret time-specific information.
  arXiv  Detail & Related papers  (2023-08-16T16:19:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.