Open AccessAnomaly detection in multidimensional time series—a graph-based approach
Author(s) -
Marcus Erz,
Jeremy Floyd Kielman,
Bahar Selvi Uzun,
Gabriele Gühring
Publication year - 2021
Publication title -
journal of physics. complexity
Language(s) - English
Resource type - Journals
ISSN - 2632-072X
DOI - 10.1088/2632-072x/ac392c
Subject(s) - anomaly detection , computer science , outlier , data mining , anomaly (physics) , cluster analysis , time series , series (stratigraphy) , graph , pattern recognition (psychology) , range (aeronautics) , artificial intelligence , machine learning , theoretical computer science , materials science , composite material , paleontology , physics , biology , condensed matter physics
As the digital transformation is taking place, more and more data is being generated and collected. To generate meaningful information and knowledge researchers use various data mining techniques. In addition to classification, clustering, and forecasting, outlier or anomaly detection is one of the most important research areas in time series analysis. In this paper we present a method for detecting anomalies in multidimensional time series using a graph-based algorithm. We transform time series data to graphs prior to calculating the outlier since it offers a wide range of graph-based methods for anomaly detection. Furthermore the dynamic of the data is taken into consideration by implementing a window of a certain size that leads to multiple graphs in different time frames. We use feature extraction and aggregation to finally compare distance measures of two time-dependent graphs. The effectiveness of our algorithm is demonstrated on the numenta anomaly benchmark with various anomaly types as well as the KPI-anomaly-detection data set of 2018 AIOps competition.