LSTP rompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting (2024)

Abstract: Time-series forecasting (TSF) finds broad applications in real-world scenarios. Prompting off-the-shelf Large Language Models (LLMs) demonstrates strong zero-shot TSF capabilities while preserving computational efficiency. However, existing prompting methods oversimplify TSF as language next-token predictions, overlooking its dynamic nature and lack of integration with state-of-the-art prompt strategies such as Chain-of-Thought. Thus, we propose LSTPrompt, a novel approach for prompting LLMs in zero-shot TSF tasks. LSTPrompt decomposes TSF into short-term and long-term forecasting sub-tasks, tailoring prompts to each. LSTPrompt guides LLMs to regularly reassess forecasting mechanisms to enhance adaptability. Extensive evaluations demonstrate consistently better performance of LSTPrompt than existing prompting methods, and competitive results compared to foundation TSF models.

## introduction time-series (ts) data are ubiquitous across various domains, including public health @xcite , finance @xcite , and energy @xcite . time-series forecasting (tsf), a crucial task in ts data analysis, aims to predict future events or trends based on historical data. recent advancements in large pre-trained models (ptms), a.k.a. foundation models, and large language models (llms) have demonstrated their effectiveness for tsf tasks. this is achieved either by training ts foundation models from scratch @xcite @xcite or adapting llms to ts data as natural language modalities @xcite @xcite . these methods leverage powerful generalization capabilities of ptms or llms, proving effectiveness in zero-shot tsf tasks with promising applications without the need for domain-specific training data. designing proper prompting techniques for zeroshot tsf tasks offers notable advantages, which avoids training models from scratch or fine-tuning llms for computational efficiency while maintaining forecasting accuracy. existing approaches @xcite prompt llms for zero-shot tsf tasks by aligning ts data with natural language sequences and prompting llms to perform tsf as sequence completion tasks. however, these methods overlook the dynamic nature of ts data and the intricate forecasting mechanisms inherent in tsf tasks, such as modeling temporal dependencies, which cannot be adequately modeled by simple sequence completion tasks. to address the limitation, we introduce lst-prompt, a novel prompt strategy of llms for tsf tasks by providing specific tsf-oriented guidelines. our contributions are summarized as follows: we provide additional related works in the appendix a with distinguishing the differences of popular zero-shot tsf methods in ## conclusion in this paper, we introduce lstprompt, a novel prompt paradigm for zero-shot tsf tasks through prompting llms. lstprompt enables llms to achieve accurate zero-shot tsf tasks through two innovative modules: timedecomp, which decomposes zero-shot tsf tasks into a series of chainof-thought subtasks, and timebreath, which en-4 7835 courages llms to periodically reassess forecasting mechanisms. extensive experiments validate the effectiveness of lstprompt, which consistently outperforms the sota prompt method and shows generally better performance than sota ptms.