REVIEW 7 cited by
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 7 Pith papers
-
Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
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.
-
CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting
CoGenCast couples a Qwen-based encoder-decoder with flow matching and reports strong MSE/MAE on ten time-series benchmarks.
-
MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning
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.
-
BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting
BALM-TSF combines a statistical-prompt text branch with a patch-based time series branch, using scaling plus contrastive alignment to balance the two modalities, improving long-term and few-shot forecasting on five of...
-
Forecasting Clicks in Digital Advertising: Multimodal Inputs and Interpretable Outputs
Adding RL-fine-tuned LLM summaries of campaign change logs to a numeric click forecaster improves MAE and RMSE on two held-out ad campaigns and produces interpretable reasoning.
-
Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives
TimesCLIP aligns image-based and text-based views of the same time series via contrastive learning to improve forecasting accuracy on several benchmarks, but the full multimodal model is not used on two of the six lon...
-
A Survey of AIOps in the Era of Large Language Models
A systematic survey that categorizes LLM-based AIOps research into four dimensions: data sources, tasks, methods, and evaluation, claiming to be the first comprehensive such overview.
Discussion (0). Sign in to comment.