TSxtend: A Tool for Batch Analysis of Temporal Sensor Data
Open Access
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
Funding Information
- FEDER/Junta de Andalucía (A-TIC-244-UGR20, PID2021-125537NA-I00, MIA.2021.M04.0008)
- Spanish Ministry of Science (A-TIC-244-UGR20, PID2021-125537NA-I00, MIA.2021.M04.0008)
- NextGenerationEU funds (A-TIC-244-UGR20, PID2021-125537NA-I00, MIA.2021.M04.0008)
This publication has 72 references indexed in Scilit:
- Quantifying self-consumption linked to solar home battery systems: Statistical analysis and economic assessmentApplied Energy, 2016
- XGBoost2016
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
- Swarm intelligence approaches to estimate electricity energy demand in TurkeyKnowledge-Based Systems, 2012
- Random Forests2012
- Greedy function approximation: A gradient boosting machine.The Annals of Statistics, 2001
- Long Short-Term MemoryNeural Computation, 1997
- The use of the area under the ROC curve in the evaluation of machine learning algorithmsPattern Recognition, 1997
- Training feedforward networks with the Marquardt algorithmIEEE Transactions on Neural Networks, 1994
- Induction of decision treesMachine Learning, 1986