教时间序列看见与言说：基于对齐视觉与文本视角的预测
Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives
AI总结：
本文提出多模态对比学习框架，将时间序列同时转化为视觉与文本视角并对齐，结合变量选择模块，在多个预测基准上超越单模态和跨模态基线。
摘要：
时间序列预测传统上依赖单模态数值输入，由于其密集且非结构化的特性，往往难以捕捉高层次的语义模式。尽管近期方法探索了利用大语言模型（LLMs）将时间序列表示为文本，但这些方法仍受限于token序列的离散特性，且缺乏人类通常运用的感知直觉，例如解读视觉模式。在本文中，我们提出了一种多模态对比学习框架，将原始时间序列转换为结构化的视觉和文本视角。我们不使用自然语言或真实世界图像，而是直接从数值序列中构建这两种模态。随后，我们通过对比学习在共享语义空间中对齐这些视图，使模型能够捕获更丰富且更具互补性的表示。此外，我们引入了一个变量选择模块，利用对齐后的表示来识别多变量预测中最具信息量的变量。在十五个短期和六个长期预测基准上的大量实验表明，我们的方法持续优于强大的单模态和跨模态基线，凸显了多模态对齐在增强时间序列预测方面的有效性。代码可在以下地址获取：https://github.com/Ironieser/TimesCLIP。
英文摘要：
Time series forecasting traditionally relies on unimodal numerical inputs, which often struggle to capture high-level semantic patterns due to their dense and unstructured nature. While recent approaches have explored representing time series as text using large language models (LLMs), these methods remain limited by the discrete nature of token sequences and lack the perceptual intuition humans typically apply, such as interpreting visual patterns. In this paper, we propose a multimodal contrastive learning framework that transforms raw time series into structured visual and textual perspectives. Rather than using natural language or real-world images, we construct both modalities directly from numerical sequences. We then align these views in a shared semantic space via contrastive learning, enabling the model to capture richer and more complementary representations. Furthermore, we introduce a variate selection module that leverages the aligned representations to identify the most informative variables for multivariate forecasting. Extensive experiments on fifteen short-term and six long-term forecasting benchmarks demonstrate that our approach consistently outperforms strong unimodal and cross-modal baselines, highlighting the effectiveness of multimodal alignment in enhancing time series forecasting. Code is available at: https://github.com/Ironieser/TimesCLIP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
