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Article

Change Point Enhanced Anomaly Detection for IoT Time Series Data

1
Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania
2
Dipartimento di Informatica, Università degli Studi di Salerno, 84084 Fisciano, Italy
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Stefano Alvisi
Water 2021, 13(12), 1633; https://doi.org/10.3390/w13121633
Received: 29 April 2021 / Revised: 26 May 2021 / Accepted: 7 June 2021 / Published: 10 June 2021
(This article belongs to the Special Issue Smart Water Solutions with Big Data)
Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance. View Full-Text
Keywords: time series; anomaly detection; change point detection; rule-based decision system; machine learning; Internet of Things; water dataset time series; anomaly detection; change point detection; rule-based decision system; machine learning; Internet of Things; water dataset
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MDPI and ACS Style

Apostol, E.-S.; Truică, C.-O.; Pop, F.; Esposito, C. Change Point Enhanced Anomaly Detection for IoT Time Series Data. Water 2021, 13, 1633. https://doi.org/10.3390/w13121633

AMA Style

Apostol E-S, Truică C-O, Pop F, Esposito C. Change Point Enhanced Anomaly Detection for IoT Time Series Data. Water. 2021; 13(12):1633. https://doi.org/10.3390/w13121633

Chicago/Turabian Style

Apostol, Elena-Simona, Ciprian-Octavian Truică, Florin Pop, and Christian Esposito. 2021. "Change Point Enhanced Anomaly Detection for IoT Time Series Data" Water 13, no. 12: 1633. https://doi.org/10.3390/w13121633

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