MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models

2025-10-27

AI Summary

Existing LLM-based time series forecasting methods overlook the statistical properties and dynamic dependencies inherent in temporal data. To address this, we propose MAP4TS—a multi-prompt fusion framework tailored for time series analysis—that explicitly encodes autocorrelation (ACF), partial autocorrelation (PACF), and Fourier spectral features as statistical prompts, and synergistically integrates them with global/local domain prompts and temporal structural prompts to bridge classical time series analysis with LLM-based reasoning. A cross-modal alignment module fuses handcrafted statistical features with raw time series embeddings. Evaluated on eight benchmark datasets, MAP4TS significantly outperforms state-of-the-art LLM-based forecasters; notably, even a lightweight GPT-2 achieves superior long-horizon forecasting accuracy compared to large LLMs. Ablation studies confirm the critical and complementary roles of all four prompt components in enhancing both prediction stability and accuracy.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Recent advances have investigated the use of pretrained large language models (LLMs) for time-series forecasting by aligning numerical inputs with LLM embedding spaces. However, existing multimodal approaches often overlook the distinct statistical properties and temporal dependencies that are fundamental to time-series data. To bridge this gap, we propose MAP4TS, a novel Multi-Aspect Prompting Framework that explicitly incorporates classical time-series analysis into the prompt design. Our framework introduces four specialized prompt components: a Global Domain Prompt that conveys dataset-level context, a Local Domain Prompt that encodes recent trends and series-specific behaviors, and a pair of Statistical and Temporal Prompts that embed handcrafted insights derived from autocorrelation (ACF), partial autocorrelation (PACF), and Fourier analysis. Multi-Aspect Prompts are combined with raw time-series embeddings and passed through a cross-modality alignment module to produce unified representations, which are then processed by an LLM and projected for final forecasting. Extensive experiments across eight diverse datasets show that MAP4TS consistently outperforms state-of-the-art LLM-based methods. Our ablation studies further reveal that prompt-aware designs significantly enhance performance stability and that GPT-2 backbones, when paired with structured prompts, outperform larger models like LLaMA in long-term forecasting tasks.

Problem

• Addresses overlooking statistical properties in time-series LLM forecasting
• Incorporates classical time-series analysis into multimodal prompt design
• Enhances forecasting accuracy and stability through structured prompting components

Innovation

• Multi-aspect prompting framework for time-series forecasting
• Incorporates classical statistical analysis into prompt design
• Cross-modality alignment module for unified representations