TS-TCD: Triplet-Level Cross-Modal Distillation for Time-Series Forecasting Using Large Language Models

• URL: http://arxiv.org/abs/2409.14978v1
• Date: Mon, 23 Sep 2024 12:57:24 GMT
• Title: TS-TCD: Triplet-Level Cross-Modal Distillation for Time-Series Forecasting Using Large Language Models
• Authors: Pengfei Wang, Huanran Zheng, Silong Dai, Wenjing Yue, Wei Zhu, Xiaoling Wang,
• Abstract summary: We present a novel framework, TS-TCD, which introduces a comprehensive three-tiered cross-modal knowledge distillation mechanism. Unlike prior work that focuses on isolated alignment techniques, our framework systematically integrates. Experiments on benchmark time-series demonstrate that TS-TCD achieves state-of-the-art results, outperforming traditional methods in both accuracy and robustness.
• Score: 15.266543423942617
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, large language models (LLMs) have shown great potential in time-series analysis by capturing complex dependencies and improving predictive performance. However, existing approaches often struggle with modality alignment, leading to suboptimal results. To address these challenges, we present a novel framework, TS-TCD, which introduces a comprehensive three-tiered cross-modal knowledge distillation mechanism. Unlike prior work that focuses on isolated alignment techniques, our framework systematically integrates: 1) Dynamic Adaptive Gating for Input Encoding and Alignment}, ensuring coherent alignment between time-series tokens and QR-decomposed textual embeddings; 2) Layer-Wise Contrastive Learning}, aligning intermediate representations across modalities to reduce feature-level discrepancies; and 3) Optimal Transport-Driven Output Alignment}, which ensures consistent output predictions through fine-grained cross-modal alignment. Extensive experiments on benchmark time-series datasets demonstrate that TS-TCD achieves state-of-the-art results, outperforming traditional methods in both accuracy and robustness.

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