Task-aware Similarity Learning for Event-triggered Time Series

TL;DR

This paper introduces an unsupervised, task-aware similarity learning framework for event-triggered time series, leveraging hierarchical autoencoders and GMM to improve similarity measurement and visualization.

Contribution

It presents a novel unsupervised framework combining hierarchical autoencoders and GMM for learning task-aware similarities in unlabeled event-triggered time series.

Findings

Outperforms state-of-the-art similarity learning methods

Effectively visualizes learned similarities

Demonstrates robustness across diverse applications

Abstract

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.