TSxtend: A Tool for Batch Analysis of Temporal Sensor Data Morcillo Jiménez, Roberto Gutiérrez Batista, Karel Gómez Romero, Juan Time series Pre-processing Prediction Machine learning Deep learning 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. 2023-03-27T11:21:48Z 2023-03-27T11:21:48Z 2023-02-04 journal article Morcillo-Jimenez, R.; Gutiérrez-Batista, K.; Gómez-Romero, J. TSxtend: A Tool for Batch Analysis of Temporal Sensor Data. Energies 2023, 16, 1581. [https://doi.org/10.3390/en16041581] https://hdl.handle.net/10481/80874 10.3390/en16041581 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI