MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models
arXiv:2510.23090v2 Announce Type: replace 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 compone
The rapid advancement and integration of large language models are pushing researchers to adapt LLMs for specialized data types like time-series, addressing current limitations in their application.
This development enhances the applicability of powerful LLMs to critical forecasting tasks across various industries, potentially improving predictive analytics and operational efficiency.
The explicit incorporation of classical time-series analysis into LLM prompting makes these models more suitable and accurate for complex temporal data, bridging a significant gap in multimodal AI approaches.
- · AI researchers and developers
- · Industries reliant on time-series forecasting (e.g., finance, logistics, energy)
- · Cloud AI service providers
- · Data scientists
- · Traditional statistical forecasting software (if not integrated with LLMs)
- · Specialized time-series ML models (if LLMs prove superior and easier to implemen
- · Companies unable to adopt advanced AI techniques
Improved accuracy and efficiency in time-series forecasting using LLMs.
Broader adoption of LLMs for predictive analytics in business and scientific domains, leading to new AI-driven products and services.
Potential for LLMs to generalize across disparate data types by effectively incorporating their inherent characteristics, accelerating general AI development.
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Read at arXiv cs.CL