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TSxtend: A Tool for Batch Analysis of Temporal Sensor Data

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  • Roberto Morcillo-Jimenez

    (Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain)

  • Karel Gutiérrez-Batista

    (Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain)

  • Juan Gómez-Romero

    (Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain)

Abstract

Pre-processing and analysis of sensor data present several challenges due to their increasingly complex structure and lack of consistency. In this paper, we present TSxtend, a software tool that allows non-programmers to transform, clean, and analyze temporal sensor data by defining and executing process workflows in a declarative language. TSxtend integrates several existing techniques for temporal data partitioning, cleaning, and imputation, along with state-of-the-art machine learning algorithms for prediction and tools for experiment definition and tracking. Moreover, the modular architecture of the tool facilitates the incorporation of additional methods. The examples presented in this paper using the ASHRAE Great Energy Predictor dataset show that TSxtend is particularly effective to analyze energy data.

Suggested Citation

  • Roberto Morcillo-Jimenez & Karel Gutiérrez-Batista & Juan Gómez-Romero, 2023. "TSxtend: A Tool for Batch Analysis of Temporal Sensor Data," Energies, MDPI, vol. 16(4), pages 1-29, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1581-:d:1058072
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    References listed on IDEAS

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