Forecasting Time Series with LLMs via Patch-Based Prompting and
  Decomposition
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.
6850d2345e07650ecce89092
Mayank Bumb
6850d2345e07650ecce89093
Anshul Vemulapalli
6850d2345e07650ecce89094
Sri Harsha Vardhan Prasad Jella
6850d2345e07650ecce89095
Anish Gupta
6850d2345e07650ecce89096
An La
6850d2345e07650ecce89097
Ryan A. Rossi
6850d2345e07650ecce89098
Hongjie Chen
6850d2345e07650ecce89099
Franck Dernoncourt
claimed_verified
2025-06-17T07:21:22.624Z
62c5947524171688a9feb992
/avatars/5a151713b9eae8dc566f5957acee3475.svg
Franck Dernoncourt
user
Franck-Dernoncourt
6850d2345e07650ecce8909a
Nesreen K. Ahmed
6850d2345e07650ecce8909b
Yu Wang
Forecasting Time Series with LLMs via Patch-Based Prompting and
  Decomposition
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.
PatchInstruct enhances LLM forecasting quality through specialized prompting methods that include time series decomposition, patch-based tokenization, and similarity-based neighbor augmentation.
Large Language Models LLMs time series forecasting prompt-based strategies time series decomposition patch-based tokenization similarity-based neighbor augmentation PatchInstruct