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Time Series Clustering: A Complex Network-Based Approach for Feature Selection in Multi-Sensor Data

Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana 118, I00184 Rome, Italy
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Modelling 2020, 1(1), 1-21; https://doi.org/10.3390/modelling1010001
Received: 15 April 2020 / Revised: 18 May 2020 / Accepted: 25 May 2020 / Published: 28 May 2020
(This article belongs to the Special Issue Feature Papers of Modelling)
Distributed monitoring sensor networks are used in an ever increasing number of applications, particularly with the advent of IoT technologies. This has led to a growing demand for unconventional analytical tools to cope with a large amount of different signals. In this scenario, the modeling of time series in similar groups represents an interesting area especially for feature subset selection (FSS) purposes. Methods based on clustering algorithms are very promising for FSS, but in their original form they are unsuitable to manage the complexity of temporal dynamics in time series. In this paper we propose a clustering approach, based on complex network analysis, for the unsupervised FSS of time series in sensor networks. We used natural visibility graphs to map signal segments in the network domain, then extracted features in the form of node degree sequences of the graphs, and finally computed time series clustering through community detection algorithms. The approach was tested on multivariate signals monitored in a 1 MW cogeneration plant and the results show that it outperforms standard time series clustering in terms of both redundancy reduction and information gain. In addition, the proposed method demonstrated its merit in terms of retention of information content with respect to the original dataset in the analyzed condition monitoring system. View Full-Text
Keywords: time series clustering; unsupervised feature subset selection; complex network; visibility graph; community detection; multivariate sensor signals; cogeneration plant time series clustering; unsupervised feature subset selection; complex network; visibility graph; community detection; multivariate sensor signals; cogeneration plant
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MDPI and ACS Style

Bonacina, F.; Miele, E.S.; Corsini, A. Time Series Clustering: A Complex Network-Based Approach for Feature Selection in Multi-Sensor Data. Modelling 2020, 1, 1-21. https://doi.org/10.3390/modelling1010001

AMA Style

Bonacina F, Miele ES, Corsini A. Time Series Clustering: A Complex Network-Based Approach for Feature Selection in Multi-Sensor Data. Modelling. 2020; 1(1):1-21. https://doi.org/10.3390/modelling1010001

Chicago/Turabian Style

Bonacina, Fabrizio; Miele, Eric S.; Corsini, Alessandro. 2020. "Time Series Clustering: A Complex Network-Based Approach for Feature Selection in Multi-Sensor Data" Modelling 1, no. 1: 1-21. https://doi.org/10.3390/modelling1010001

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