  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.
