TSxtend: A Tool for Batch Analysis of Temporal Sensor Data

Roberto Morcillo-Jimenez, Karel Gutiérrez-Batista and Juan Gómez-Romero ()
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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

Energies, 2023, vol. 16, issue 4, 1-29

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.

Keywords: time series; pre-processing; prediction; machine learning; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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