TMFS-MTS: Toolbox for metaheuristic feature selection in multivariate time series
Year: 2025
Type: article
Abstract: Feature selection in multivariate time series is a key challenge in modern data analysis, as high-dimensional data often include temporal dependencies and irrelevant features degrading classifier performance. To address these issues, a comparison with existing approaches is essential. Therefore, this work introduces the Toolbox for Metaheuristic Feature Selection in Multivariate Time Series (TMFS-MTS), providing an environment for feature selection and metaheuristic evaluation. It supports diverse fitness measures and advanced visualizations, including convergence curves, feature count tracking, runtime analysis, Wilcoxon tests, and 2D embeddings. Implemented in MATLAB, TMFS-MTS offers a standardized framework for advancing research in multivariate time series feature selection. • Toolbox for metaheuristic feature selection in multivariate time series. • Advanced visualization and benchmarking capabilities. • Open-source MATLAB implementation on GitHub.
Source: Software Impacts
Authors: Mariusz Oszust, Marian Wysocki
Institution: Rzeszów University of Technology
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $600
OpenAlex ID: W7083330373