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Probabilistic anomaly detection in natural gas time series data

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  • Akouemo, Hermine N.
  • Povinelli, Richard J.

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.

Suggested Citation

  • Akouemo, Hermine N. & Povinelli, Richard J., 2016. "Probabilistic anomaly detection in natural gas time series data," International Journal of Forecasting, Elsevier, vol. 32(3), pages 948-956.
    • Handle: RePEc:eee:intfor:v:32:y:2016:i:3:p:948-956
    DOI: 10.1016/j.ijforecast.2015.06.001
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    Cited by:

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      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
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