从图像到信号：大型视觉模型对时间序列分析有用吗？
From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?
AI总结：
本文首次系统评估大型视觉模型（LVMs）在时间序列分析中的效用，发现其在分类任务中有用但在预测任务中面临挑战，并揭示了现有方法的局限性，为未来多模态时间序列研究奠定基础。
摘要：
基于Transformer的模型在时间序列研究中日益受到关注，推动了大型语言模型（LLMs）和基础模型在时间序列分析中的研究兴趣。随着该领域向多模态方向发展，大型视觉模型（LVMs）正成为一个有前景的方向。过去，Transformer和LLMs在时间序列中的有效性一直存在争议。当涉及LVMs时，类似的问题出现了：LVMs对时间序列分析真的有用吗？为了解决这个问题，我们设计并进行了首个原则性研究，涉及4个LVMs、8种成像方法、18个数据集和26个基线，涵盖高层（分类）和低层（预测）任务，并进行了广泛的消融分析。我们的研究结果表明，LVMs确实对时间序列分类有用，但在预测方面面临挑战。尽管有效，当代最佳的LVM预测器仅限于特定类型的LVMs和成像方法，表现出对预测周期的偏见，并且利用长回看窗口的能力有限。我们希望我们的发现能够为未来基于LVM和多模态解决方案在不同时间序列任务中的研究奠定基础。
英文摘要：
Transformer-based models have gained increasing attention in time series research, driving interest in Large Language Models (LLMs) and foundation models for time series analysis. As the field moves toward multi-modality, Large Vision Models (LVMs) are emerging as a promising direction. In the past, the effectiveness of Transformer and LLMs in time series has been debated. When it comes to LVMs, a similar question arises: are LVMs truely useful for time series analysis? To address it, we design and conduct the first principled study involving 4 LVMs, 8 imaging methods, 18 datasets and 26 baselines across both high-level (classification) and low-level (forecasting) tasks, with extensive ablation analysis. Our findings indicate LVMs are indeed useful for time series classification but face challenges in forecasting. Although effective, the contemporary best LVM forecasters are limited to specific types of LVMs and imaging methods, exhibit a bias toward forecasting periods, and have limited ability to utilize long look-back windows. We hope our findings could serve as a cornerstone for future research on LVM- and multimodal-based solutions to different time series tasks.
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。
