TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series

by   Yang Jiao, et al.
NEC Laboratories America
Tongji University

Multivariate time series (MTS) data are becoming increasingly ubiquitous in diverse domains, e.g., IoT systems, health informatics, and 5G networks. To obtain an effective representation of MTS data, it is not only essential to consider unpredictable dynamics and highly variable lengths of these data but also important to address the irregularities in the sampling rates of MTS. Existing parametric approaches rely on manual hyperparameter tuning and may cost a huge amount of labor effort. Therefore, it is desirable to learn the representation automatically and efficiently. To this end, we propose an autonomous representation learning approach for multivariate time series (TimeAutoML) with irregular sampling rates and variable lengths. As opposed to previous works, we first present a representation learning pipeline in which the configuration and hyperparameter optimization are fully automatic and can be tailored for various tasks, e.g., anomaly detection, clustering, etc. Next, a negative sample generation approach and an auxiliary classification task are developed and integrated within TimeAutoML to enhance its representation capability. Extensive empirical studies on real-world datasets demonstrate that the proposed TimeAutoML outperforms competing approaches on various tasks by a large margin. In fact, it achieves the best anomaly detection performance among all comparison algorithms on 78 out of all 85 UCR datasets, acquiring up to 20 performance improvement in terms of AUC score.


page 4

page 5

page 6

page 8

page 9

page 10

page 11

page 13


Time-Series Anomaly Detection with Implicit Neural Representation

Detecting anomalies in multivariate time-series data is essential in man...

Contrastive Shapelet Learning for Unsupervised Multivariate Time Series Representation Learning

Recent studies have shown great promise in unsupervised representation l...

Unsupervised Scalable Representation Learning for Multivariate Time Series

Time series constitute a challenging data type for machine learning algo...

Effective Abnormal Activity Detection on Multivariate Time Series Healthcare Data

Multivariate time series (MTS) data collected from multiple sensors prov...

Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series

Subsequence anomaly detection in long sequences is an important problem ...

Similarity Preserving Representation Learning for Time Series Analysis

A considerable amount of machine learning algorithms take instance-featu...

Please sign up or login with your details

Forgot password? Click here to reset