SMArT: A Self-Driven Multiview Architecture Leveraging Large Language Models for Time Series Forecasting.
TL;DR
SMArT enriches intrapatch semantics through self-supervised multiview fusion, and establishes an effective framework for TSF by integrating self-driven multiview learning with LLMs' reasoning power.
Abstract
Time series forecasting (TSF) is critical in numerous real-world applications, yet its sequential scalar presentation limits semantic richness and the capture of complex temporal patterns. Recent advances leveraging patchwise modeling and pretrained large language models (LLMs) have achieved notable progress. However, existing methods largely focus on raw sequential patterns while overlooking intrapatch semantics, which limits the richness of time series representations, and they fail to effectively exploit complementary information from multiple views. To tackle these challenges, we propose a self-driven multiview architecture for TSF (SMArT). SMArT enriches intrapatch semantics through self-supervised multiview fusion. It jointly captures fine-grained temporal 1-D dependencies via pointwise self-attention and global temporal 2-D structures via Gramian angular fields (GAFs), all without requiring external supervision. To further bridge the gap between time series data and LLMs, SMArT pioneers a dual-prompt strategy, combining static, dataset-level guidance with dynamic, input-specific prompts derived from frequency-domain decomposition, significantly enhancing LLMs' adaptability and generalization. Extensive experiments across diverse TSF tasks validate SMArT's robustness, achieving state-of-the-art performance in long-term forecasting and excelling in few-shot and zero-shot scenarios. By integrating self-driven multiview learning with LLMs' reasoning power, SMArT establishes an effective framework for TSF. Our code and Supplementary Materials are available at https://github.com/BMRETURN/SMArT.