Feature Construction Using Persistence Landscapes for Clustering Noisy IoT Time Series

Renjie Chen and Nalini Ravishanker ()
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Renjie Chen: Department of Statistics, University of Connecticut, Storrs, CT 06269, USA
Nalini Ravishanker: Department of Statistics, University of Connecticut, Storrs, CT 06269, USA

Future Internet, 2023, vol. 15, issue 6, 1-13

Abstract: With the advancement of IoT technologies, there is a large amount of data available from wireless sensor networks (WSN), particularly for studying climate change. Clustering long and noisy time series has become an important research area for analyzing this data. This paper proposes a feature-based clustering approach using topological data analysis, which is a set of methods for finding topological structure in data. Persistence diagrams and landscapes are popular topological summaries that can be used to cluster time series. This paper presents a framework for selecting an optimal number of persistence landscapes, and using them as features in an unsupervised learning algorithm. This approach reduces computational cost while maintaining accuracy. The clustering approach was demonstrated to be accurate on simulated data, based on only four, three, and three features, respectively, selected in Scenarios 1–3. On real data, consisting of multiple long temperature streams from various US locations, our optimal feature selection method achieved approximately a 13 times speed-up in computing.

Keywords: elbow method; feature construction; IoT time series; persistence landscape; topological data analysis; unsupervised learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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