Time-Series Data Curation: Design, Implementation, and Performance Evaluation of a General-Purpose Toolbox

As prediction models for automated decision-making in manufacturing become more widespread, enhancing their reliability has become increasingly critical. One effective approach is improving the quality of training data through data curation or cleaning. However, most existing data curation methods rely on domain-specific expertise in data science, limiting their accessibility. To address this challenge, a general-purpose time-series data curation toolbox with a user-friendly interface was developed. Designed to minimize user input while maintaining traceability, the toolbox was tested on various publicly available time-series datasets. Its performance was evaluated by comparing the prediction accuracy of machine learning models trained on original versus cleaned data. The results demonstrate the significant potential of the general-purpose toolbox for enhancing data quality and model performance across a range of applications.