Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition

• URL: http://arxiv.org/abs/2506.12953v1
• Date: Sun, 15 Jun 2025 19:42:58 GMT
• Title: Forecasting Time Series with LLMs via Patch-Based Prompting and Decomposition
• Authors: Mayank Bumb, Anshul Vemulapalli, Sri Harsha Vardhan Prasad Jella, Anish Gupta, An La, Ryan A. Rossi, Hongjie Chen, Franck Dernoncourt, Nesreen K. Ahmed, Yu Wang,
• Abstract summary: We explore simple and flexible prompt-based strategies that enable LLMs to perform time series forecasting without extensive retraining.<n>We propose our own method, PatchInstruct, which enables LLMs to make precise and effective predictions.
• Score: 48.50019311384125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Large Language Models (LLMs) have demonstrated new possibilities for accurate and efficient time series analysis, but prior work often required heavy fine-tuning and/or ignored inter-series correlations. In this work, we explore simple and flexible prompt-based strategies that enable LLMs to perform time series forecasting without extensive retraining or the use of a complex external architecture. Through the exploration of specialized prompting methods that leverage time series decomposition, patch-based tokenization, and similarity-based neighbor augmentation, we find that it is possible to enhance LLM forecasting quality while maintaining simplicity and requiring minimal preprocessing of data. To this end, we propose our own method, PatchInstruct, which enables LLMs to make precise and effective predictions.

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