Probabilistic anomaly detection in natural gas time series data

Hermine N. Akouemo and Richard J. Povinelli

International Journal of Forecasting, 2016, vol. 32, issue 3, 948-956

Abstract: 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.

Keywords: Data cleaning; Energy; Outlier detection; Linear regression; Bayesian classifier; Gaussian mixture models (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:32:y:2016:i:3:p:948-956

DOI: 10.1016/j.ijforecast.2015.06.001

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