Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Online Anomaly Detection for Smartphone-Based Multivariate Behavioral Time Series Data

Version 1 : Received: 4 February 2022 / Approved: 7 February 2022 / Online: 7 February 2022 (11:54:54 CET)

A peer-reviewed article of this Preprint also exists.

Liu, G.; Onnela, J.-P. Online Anomaly Detection for Smartphone-Based Multivariate Behavioral Time Series Data. Sensors 2022, 22, 2110. Liu, G.; Onnela, J.-P. Online Anomaly Detection for Smartphone-Based Multivariate Behavioral Time Series Data. Sensors 2022, 22, 2110.

Journal reference: Sensors 2022, 22, 2110
DOI: 10.3390/s22062110

Abstract

To detect aberrant human behaviors from large volume of passive data collected by smartphones in real time, we propose an online anomaly detection method using Hotelling’s T-squared test. The test statistic is a weighted average, with more weight on the between-individual component when there are little data available for the individual and more weight on the within-individual component when the data are adequate. The algorithm takes only O(1) run time in each update and the required memory usage is fixed after a pre-specified number of updates. The performance of the proposed method, in terms of accuracy, sensitivity and specificity, is consistently better than or equal to the offline method that it builds upon depending on the sample size of the individual data.

Keywords

Online learning; Anomaly detection; Hotelling’s T-squared test; Digital phenotyping

Subject

MATHEMATICS & COMPUTER SCIENCE, Probability and Statistics

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