Format of Original
International Journal of Forecasting
Original Item ID
This paper introduces a probabilistic approach to anomaly detection, specifically in natural gas time series data. In the natural gas field, there are various types of anomalies, each of which is induced by a range of causes and sources. The causes of a set of anomalies are examined and categorized, and a Bayesian maximum likelihood classifier learns the temporal structures of known anomalies. Given previously unseen time series data, the system detects anomalies using a linear regression model with weather inputs, after which the anomalies are tested for false positives and classified using a Bayesian classifier. The method can also identify anomalies of an unknown origin. Thus, the likelihood of a data point being anomalous is given for anomalies of both known and unknown origins. This probabilistic anomaly detection method is tested on a reported natural gas consumption data set.
Akouemo Kengmo Kenfack, Hermine Nathalie and Povinelli, Richard J., "Probabilistic Anomaly Detection in Natural Gas Time Series Data" (2016). Electrical and Computer Engineering Faculty Research and Publications. 146.