  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.
