用自监督学习加速时间序列对齐，比传统方法更快更准。
TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning
- 通过自监督学习提取关键点与描述子，实现高效对齐。
- 在合成数据上训练后，对真实数据仍具强泛化能力，速度提升显著。
- 适合需要快速高精度对齐的工业级时间序列分析场景。
快速且可扩展的时间序列对齐是多个领域的基础挑战。标准方法动态时间规整（DTW）存在可扩展性差和对噪声敏感的问题。我们提出TimePoint，一种自监督方法，通过从合成数据中学习关键点与描述子，显著加速基于DTW的对齐，同时通常提升对齐精度。受二维关键点检测启发，但针对一维信号特点进行了适配，采用高效的1D微分同胚生成真实感训练数据。结合全卷积与小波卷积架构，有效提取具有信息量的关键点与描述子。将DTW应用于这些稀疏表示，实现大幅提速，并通常优于直接对完整信号使用标准DTW的精度。仅在合成数据上训练的TimePoint在真实时间序列上表现出强泛化能力，进一步在真实数据上微调可提升性能。大量实验表明，TimePoint始终在速度与准确性上优于标准DTW，为时间序列分析提供可扩展解决方案。代码已公开于 https://github.com/BGU-CS-VIL/TimePoint。
原文摘要 · Abstract (English)
Fast and scalable alignment of time series is a fundamental challenge in many domains. The standard solution, Dynamic Time Warping (DTW), struggles with poor scalability and sensitivity to noise. We introduce TimePoint, a self-supervised method that dramatically accelerates DTW-based alignment while typically improving alignment accuracy by learning keypoints and descriptors from synthetic data. Inspired by 2D keypoint detection but carefully adapted to the unique challenges of 1D signals, TimePoint leverages efficient 1D diffeomorphisms, which effectively model nonlinear time warping, to generate realistic training data. This approach, along with fully convolutional and wavelet convolutional architectures, enables the extraction of informative keypoints and descriptors. Applying DTW to these sparse representations yield major speedups and typically higher alignment accuracy than standard DTW applied to the full signals. TimePoint demonstrates strong generalization to real-world time series when trained solely on synthetic data, and further improves with fine-tuning on real data. Extensive experiments demonstrate that TimePoint consistently achieves faster and more accurate alignments than standard DTW, making it a scalable solution for time-series analysis. Our code is available at https://github.com/BGU-CS-VIL/TimePoint
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。