PatchInstruct enhances LLM forecasting quality through specialized prompting methods that include time series decomposition, patch-based tokenization, and similarity-based neighbor augmentation.
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.
