Modeling multiple time series for anomaly detection

By Philip Ka-Fai Chan, Matthew V. Mahoney. 2005

135 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Signal Processing

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Citations in the corpus

One-Class Support Measure Machines for Group Anomaly Detection (2013, methodology): “The anomaly detection has been intensively studied (Chandola et al. (2009) and references therein).”
Group Anomaly Detection using Flexible Genre Models (2011, methodology): “To handle distribution-based anomalies, we can design features for groups and then treat them as points [5, 6].”
A least-squares approach to anomaly detection in static and sequential data (2014, result): “For comparison, previous work (Chan and Mahoney, 2005) has shown other anomaly detection methods unable to perfectly separate normal and abnormal sequences even when training on three normal sequences, and perfect separation to be pos- sible when training on two normal sequences at a time using a…”
Time-series clustering - A decade review (2015, methodology): “Briefly, finding the clusters of time-series can be advantageous in different domains to answer following real world problems: Anomaly, novelty or discord detection: Anomaly detection are methods to discover unusual and unexpected patterns which happen in datasets surprisingly [31–34].”
Support Measure Data Description for group anomaly detection (2015, background): “Several solutions have been proposed to this kind of estimation, including representing groups by sets of features [3, 14], or estimating group anomalies by clustering point anomalies [5].”
Hierarchical Probabilistic Models for Group Anomaly Detection (2011, background): “To do this, we need to deﬁne a set of features for the groups (Chan and Mahoney, 2005; Keogh et al., 2005).”
A review on time series data mining (2011, methodology): “Chan and Mahoney (2005) present an online anomaly detection approach based on the Gecko algorithm, which creates a sequence of minimal bounding boxes with the training trajectories.”
Support Measure Data Description A One-Class Classifier for Group Anomaly Detection (2017, unknown): “apply an anomaly detector on the induced feature space [3, 15].”
Anomaly detection: A survey (2009, methodology): “A transformation technique has been proposed for multiple time-series data [Chan and Mahoney 2005], known as Box Modeling.”