TSxtend: A Tool for Batch Analysis of Temporal Sensor Data

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 pr...

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Vydané v:	Energies (Basel) Ročník 16; číslo 4; s. 1581
Hlavní autori:	Morcillo-Jimenez, Roberto, Gutiérrez-Batista, Karel, Gómez-Romero, Juan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.02.2023
Predmet:	Algorithms Artificial intelligence Batch processing Data science Decision making deep learning Electronic data processing Energy consumption Knowledge Libraries machine learning Methods pre-processing prediction Remote sensing Sensors Time series Trends Spain
ISSN:	1996-1073, 1996-1073
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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.
AbstractList	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.
Audience	Academic
Author	Morcillo-Jimenez, Roberto Gutiérrez-Batista, Karel Gómez-Romero, Juan
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SubjectTerms	Algorithms Artificial intelligence Batch processing Data science Decision making deep learning Electronic data processing Energy consumption Knowledge Libraries machine learning Methods pre-processing prediction Remote sensing Sensors Time series Trends
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Title	TSxtend: A Tool for Batch Analysis of Temporal Sensor Data
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