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Feature Construction Using Persistence Landscapes for Clustering Noisy IoT Time Series

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  • Renjie Chen

    (Department of Statistics, University of Connecticut, Storrs, CT 06269, USA
    These authors contributed equally to this work.)

  • Nalini Ravishanker

    (Department of Statistics, University of Connecticut, Storrs, CT 06269, USA
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Renjie Chen & Nalini Ravishanker, 2023. "Feature Construction Using Persistence Landscapes for Clustering Noisy IoT Time Series," Future Internet, MDPI, vol. 15(6), pages 1-13, May.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:6:p:195-:d:1157875
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    References listed on IDEAS

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    1. Yunsheng Zhang & Qingzhang Shi & Jiawei Zhu & Jian Peng & Haifeng Li, 2021. "Time Series Clustering with Topological and Geometric Mixed Distance," Mathematics, MDPI, vol. 9(9), pages 1-17, May.
    2. Elad Levintal & Kosana Suvočarev & Gail Taylor & Helen E. Dahlke, 2021. "Embrace open-source sensors for local climate studies," Nature, Nature, vol. 599(7883), pages 32-32, November.
    3. Gidea, Marian & Goldsmith, Daniel & Katz, Yuri & Roldan, Pablo & Shmalo, Yonah, 2020. "Topological recognition of critical transitions in time series of cryptocurrencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
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    Cited by:

    1. Xiangzeng Kong & Xinyue Liu & Shimiao Chen & Wenxuan Kang & Zhicong Luo & Jianjun Chen & Tao Wu, 2024. "Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models," Mathematics, MDPI, vol. 12(2), pages 1-17, January.

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