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LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting

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arxiv 2402.16132 v1 pith:EBDIN57O submitted 2024-02-25 cs.CL cs.AI

LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting

Haoxin Liu , Zhiyuan Zhao , Jindong Wang , Harshavardhan Kamarthi , B. Aditya Prakash This is my paper
classification cs.CL cs.AI
keywords lstpromptpromptingforecastinglanguagellmsmodelszero-shotexisting
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. From Time Series Analysis to Question Answering: A Survey in the LLM Era

    cs.LG 2025-06 accept novelty 6.0

    A survey proposing a taxonomy of Injective, Bridging, and Internal Alignment paradigms to evolve TSA into user-driven Time Series Question Answering with LLMs.

  2. Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs

    cs.LG 2025-06 unverdicted novelty 6.0

    Time-R1 trains LLMs via supervised fine-tuning followed by reinforcement learning with a time-series-specific reward and non-uniform GRIP sampling to enable multi-step reasoning that improves forecasting accuracy.

  3. CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting

    cs.LG 2026-02 conditional novelty 5.0

    CoGenCast couples a Qwen-based encoder-decoder with flow matching and reports strong MSE/MAE on ten time-series benchmarks.

  4. MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning

    cs.LG 2026-02 reject novelty 5.0

    MemCast claims LLM time-series forecasting improves when retrieval from a hierarchical memory of patterns, wisdom, and laws conditions reasoning, but the reported gains depend on a test-label-rewarded confidence update.