RoLA: A Real-Time Online Lightweight Anomaly Detection System for
  Multivariate Time Series
Ming-Chang Lee Jia-Chun Lin
A multivariate time series refers to observations of two or more variables
taken from a device or a system simultaneously over time. There is an
increasing need to monitor multivariate time series and detect anomalies in
real time to ensure proper system operation and good service quality. It is
also highly desirable to have a lightweight anomaly detection system that
considers correlations between different variables, adapts to changes in the
pattern of the multivariate time series, offers immediate responses, and
provides supportive information regarding detection results based on
unsupervised learning and online model training. In the past decade, many
multivariate time series anomaly detection approaches have been introduced.
However, they are unable to offer all the above-mentioned features. In this
paper, we propose RoLA, a real-time online lightweight anomaly detection system
for multivariate time series based on a divide-and-conquer strategy, parallel
processing, and the majority rule. RoLA employs multiple lightweight anomaly
detectors to monitor multivariate time series in parallel, determine the
correlations between variables dynamically on the fly, and then jointly detect
anomalies based on the majority rule in real time. To demonstrate the
performance of RoLA, we conducted an experiment based on a public dataset
provided by the FerryBox of the One Ocean Expedition. The results show that
RoLA provides satisfactory detection accuracy and lightweight performance.