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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Energies (Basel) Ročník 16; číslo 4; s. 1581
Hlavní autoři: Morcillo-Jimenez, Roberto, Gutiérrez-Batista, Karel, Gómez-Romero, Juan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.02.2023
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en16041581