Abstract
Detecting anomalous readings in data is a problem. Humans are good at some types, for example with images, however machines find it rather more difficult. Detecting anomalies in time series data is even more tricky. Discriminating between data that is part of the same distribution, or caused by some other process is also nontrivial. Anomaly detection is used in a wide range of applications, for example fraud detection for bank accounts, condition monitoring of mechanical systems, and in medical imagery. In all these applications, an outlier is indicative of a problem that requires further attention. A range of outlier detection methods is presented, and tested on a range of synthetic multivariate time series data. A novel method, cyclic regression, is presented and compared to more traditional methods. The application of these methods to real world data is demonstrated.
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