通过捕捉序列中的概念跃迁，实现动态行为的精准识别与定位。
Wormhole: Concept-Aware Deep Representation Learning for Co-Evolving Sequences
- 引入自表示层与时间平滑约束，提升概念识别稳定性。
- 通过潜在空间突变检测概念跃迁位置，定位精度高。
- 适合分析金融、物联网等复杂时序数据中的概念漂移。
识别共演化序列中的动态概念对分析物联网应用、金融市场和在线活动日志等复杂系统至关重要。这些概念揭示了序列数据的内在结构与行为模式，有助于改进决策与预测。本文提出Wormhole，一种面向共演化时间序列的概念感知深度表征学习框架。该模型包含自表示层与时间平滑约束，确保动态概念及其转换的鲁棒识别。概念转换通过潜在空间中的突变进行检测，标志着行为的显著转变——如同穿越虫洞。这一机制可准确识别共演化序列中的概念，并精确定位其转换位置，显著提升表征的可解释性。实验表明，该方法能有效将时间序列分割为有意义的概念，为分析复杂时序模式及检测概念漂移提供了有力工具。
原文摘要 · Abstract (English)
Identifying and understanding dynamic concepts in co-evolving sequences is crucial for analyzing complex systems such as IoT applications, financial markets, and online activity logs. These concepts provide valuable insights into the underlying structures and behaviors of sequential data, enabling better decision-making and forecasting. This paper introduces Wormhole, a novel deep representation learning framework that is concept-aware and designed for co-evolving time sequences. Our model presents a self-representation layer and a temporal smoothness constraint to ensure robust identification of dynamic concepts and their transitions. Additionally, concept transitions are detected by identifying abrupt changes in the latent space, signifying a shift to new behavior - akin to passing through a wormhole. This novel mechanism accurately discerns concepts within co-evolving sequences and pinpoints the exact locations of these wormholes, enhancing the interpretability of the learned representations. Experiments demonstrate that this method can effectively segment time series data into meaningful concepts, providing a valuable tool for analyzing complex temporal patterns and advancing the detection of concept drifts.
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