Document Type

Article

Language

eng

Format of Original

9 p.

Publication Date

7-2016

Publisher

Elsevier

Source Publication

International Journal of Forecasting

Source ISSN

0169-2070

Original Item ID

DOI: 10.1016/j.ijforecast.2015.06.001

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.

Comments

Accepted version. International Journal of Forecasting, Vol. 32, No. 3 (July/September 2016): 948-956. DOI. © 2015 International Institute of Forecasters. Published by Elsevier B.V. Used with permission.

This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, Vol. 32, No. 3 (July/September 2016): 948-956. DOI.

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