Task-aware Similarity Learning for Event-triggered Time Series
This work addresses a crucial problem for applications like automated driving and smart home automation by providing a systematic approach to similarity learning, though it appears incremental as it builds on existing techniques like autoencoders and GMMs.
The paper tackles the problem of learning suitable similarity measures for event-triggered time series, which is challenging due to complex temporal dynamics, by proposing an unsupervised framework that combines hierarchical multi-scale sequence autoencoders and Gaussian Mixture Models, resulting in a method that outperforms state-of-the-art approaches in experiments.
Time series analysis has achieved great success in diverse applications such as network security, environmental monitoring, and medical informatics. Learning similarities among different time series is a crucial problem since it serves as the foundation for downstream analysis such as clustering and anomaly detection. It often remains unclear what kind of distance metric is suitable for similarity learning due to the complex temporal dynamics of the time series generated from event-triggered sensing, which is common in diverse applications, including automated driving, interactive healthcare, and smart home automation. The overarching goal of this paper is to develop an unsupervised learning framework that is capable of learning task-aware similarities among unlabeled event-triggered time series. From the machine learning vantage point, the proposed framework harnesses the power of both hierarchical multi-scale sequence autoencoders and Gaussian Mixture Model (GMM) to effectively learn the low-dimensional representations from the time series. Finally, the obtained similarity measure can be easily visualized for explaining. The proposed framework aspires to offer a stepping stone that gives rise to a systematic approach to model and learn similarities among a multitude of event-triggered time series. Through extensive qualitative and quantitative experiments, it is revealed that the proposed method outperforms state-of-the-art methods considerably.