TopoCL：面向时间序列的拓扑对比学习
TopoCL: Topological Contrastive Learning for Time Series
AI总结：
本文提出TopoCL，通过引入持久同调构建时间序列的拓扑特征，并联合优化时间模态对比学习与时间-拓扑对应关系，在分类、异常检测、预测和迁移学习任务上取得最先进性能。
摘要：
通用时间序列表示学习具有挑战性，但在分类、异常检测和预测等实际应用中极具价值。近年来，对比学习（CL）已被积极探索用于处理时间序列表示。然而，一个关键挑战在于，CL中的数据增强过程可能会扭曲季节性模式或时间依赖性，不可避免地导致语义信息丢失。为应对这一挑战，我们提出了面向时间序列的拓扑对比学习（TopoCL）。TopoCL通过引入持久同调来缓解此类信息丢失，持久同调能够捕获数据在变换下保持不变的拓扑特征。在本文中，我们将时间序列数据的时间属性和拓扑属性视为不同的模态。具体而言，我们计算持久同调以构建时间序列数据的拓扑特征，并将其表示为持久图。然后，我们设计了一个神经网络来编码这些持久图。我们的方法联合优化时间模态内的CL以及时间-拓扑对应关系，促进对时间序列的时间语义和拓扑属性的全面理解。我们在四个下游任务上进行了广泛实验——分类、异常检测、预测和迁移学习。结果表明，TopoCL达到了最先进的性能。
英文摘要：
Universal time series representation learning is challenging but valuable in real-world applications such as classification, anomaly detection, and forecasting. Recently, contrastive learning (CL) has been actively explored to tackle time series representation. However, a key challenge is that the data augmentation process in CL can distort seasonal patterns or temporal dependencies, inevitably leading to a loss of semantic information. To address this challenge, we propose Topological Contrastive Learning for time series (TopoCL). TopoCL mitigates such information loss by incorporating persistent homology, which captures the topological characteristics of data that remain invariant under transformations. In this paper, we treat the temporal and topological properties of time series data as distinct modalities. Specifically, we compute persistent homology to construct topological features of time series data, representing them in persistence diagrams. We then design a neural network to encode these persistent diagrams. Our approach jointly optimizes CL within the time modality and time-topology correspondence, promoting a comprehensive understanding of both temporal semantics and topological properties of time series. We conduct extensive experiments on four downstream tasks-classification, anomaly detection, forecasting, and transfer learning. The results demonstrate that TopoCL achieves state-of-the-art performance.
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。
