Abstract
The present disclosure describes a method and system for detecting anomalies in multidimensional time series data using multidimensional matrix profiles are disclosed. A computer system can generate a multidimensional matrix profile corresponding to a multidimensional time series using a pre-sorting or post-sorting technique. The computer system can detect anomalies in the multidimensional time series using the multidimensional matrix profile, e.g., using thresholding or machine learning. The computer system can issue an alert to a requestor if an anomaly has been detected. Additionally disclosed is a k-nearest-neighbour extension to the multidimensional matrix profile anomaly detection methods.
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Recommended Citation
Yeh, Michael Visa; Dai, Xin Visa; Zheng, Yan visa; Zhuang, Zhongfang Visa; Wang, Laing Visa; Fan, Yujie Visa; Wang, Junpeng Visa; Aboagye, Prince Osei Visa; Saini, Uday Singh Visa; and Zhang, Wei Visa, "ANOMALY DETECTION ON MULTIDIMENSIONAL TIME SERIES", Technical Disclosure Commons, (January 07, 2025)
https://www.tdcommons.org/dpubs_series/7701