# A Feature Selection Method for Multi-Dimension Time-Series Data

arXiv: https://arxiv.org/abs/2104.11110 · HTML: https://mlsanity.com/paper/2104.11110/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 22 Apr 2021.

## Builds on (most cited first, 1 of 1)

- [The UEA multivariate time series classification archive, 2018](https://mlsanity.com/paper/1811.00075/) (2018)

## Built on by (most cited first, 5 of 5)

- [Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series Classification](https://mlsanity.com/paper/2206.09274/) (2022)
- [Correlation Based Feature Subset Selection for Multivariate Time-Series Data](https://mlsanity.com/paper/2112.03705/) (2021)
- [A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant information](https://mlsanity.com/paper/2411.12570/) (2024)
- [Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach](https://mlsanity.com/paper/2506.20359/) (2025)
- [Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals](https://mlsanity.com/paper/2605.18483/) (2026)

## Abstract (arXiv)

Time-series data in application areas such as motion capture and activity recognition is often multi-dimension. In these application areas data typically comes from wearable sensors or is extracted from video. There is a lot of redundancy in these data streams and good classification accuracy will often be achievable with a small number of features (dimensions). In this paper we present a method for feature subset selection on multidimensional time-series data based on mutual information. This method calculates a merit score (MSTS) based on correlation patterns of the outputs of classifiers trained on single features and the `best' subset is selected accordingly. MSTS was found to be significantly more efficient in terms of computational cost while also managing to maintain a good overall accuracy when compared to Wrapper-based feature selection, a feature selection strategy that is popular elsewhere in Machine Learning. We describe the motivations behind this feature selection strategy and evaluate its effectiveness on six time series datasets.