# Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series Classification

arXiv: https://arxiv.org/abs/2206.09274 · HTML: https://mlsanity.com/paper/2206.09274/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 18 Jun 2022.

## Builds on (most cited first, 10 of 10)

- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [Benchmarking Multivariate Time Series Classification Algorithms](https://mlsanity.com/paper/2007.13156/) (2020)
- [Should we really use post-hoc tests based on mean-ranks?](https://mlsanity.com/paper/1505.02288/) (2015)
- [sktime: A Unified Interface for Machine Learning with Time Series](https://mlsanity.com/paper/1909.07872/) (2019)
- [Benchmarking Deep Learning Interpretability in Time Series Predictions](https://mlsanity.com/paper/2010.13924/) (2020)
- [Multivariate Time Series Classification with WEASEL+MUSE](https://mlsanity.com/paper/1711.11343/) (2017)
- [Fast and Accurate Time Series Classification with WEASEL](https://mlsanity.com/paper/1701.07681/) (2017)
- [Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic Representations](https://mlsanity.com/paper/2006.01667/) (2020)
- [A Feature Selection Method for Multi-Dimension Time-Series Data](https://mlsanity.com/paper/2104.11110/) (2021)
- [Supervised Feature Subset Selection and Feature Ranking for Multivariate Time Series without Feature Extraction](https://mlsanity.com/paper/2005.00259/) (2020)

## Built on by (most cited first, 8 of 8)

- [aeon: a Python toolkit for learning from time series](https://mlsanity.com/paper/2406.14231/) (2024)
- [Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification](https://mlsanity.com/paper/2406.12507/) (2024)
- [An Examination of Wearable Sensors and Video Data Capture for Human Exercise Classification](https://mlsanity.com/paper/2307.04516/) (2023)
- [Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering](https://mlsanity.com/paper/2411.17838/) (2024)
- [An Empirical Evaluation of Factors Affecting SHAP Explanation of Time Series Classification](https://mlsanity.com/paper/2509.03649/) (2025)
- [PRISM: Lightweight Multivariate Time-Series Classification through Symmetric Multi-Resolution Convolutional Layers](https://mlsanity.com/paper/2508.04503/) (2025)
- [Scaling Time Series Classification via XAI-Driven Data Reduction](https://mlsanity.com/paper/2607.15774/) (2026)
- [Improving Multivariate Time Series Classification with Class-Wise Training and Model Aggregation](https://mlsanity.com/paper/2609.07493/) (2026)

## Abstract (arXiv)

Accuracy is a key focus of current work in time series classification. However, speed and data reduction in many applications is equally important, especially when the data scale and storage requirements increase rapidly. Current MTSC algorithms need hundreds of compute hours to complete training and prediction. This is due to the nature of multivariate time series data, which grows with the number of time series, their length and the number of channels. In many applications, not all the channels are useful for the classification task; hence we require methods that can efficiently select useful channels and thus save computational resources. We propose and evaluate two methods for channel selection. Our techniques work by representing each class by a prototype time series and performing channel selection based on the prototype distance between classes. The main hypothesis is that useful channels enable better separation between classes; hence, channels with the higher distance between class prototypes are more useful. On the UEA Multivariate Time Series Classification (MTSC) benchmark, we show that these techniques achieve significant data reduction and classifier speedup for similar levels of classification accuracy. Channel selection is applied as a pre-processing step before training state-of-the-art MTSC algorithms and saves about 70% of computation time and data storage, with preserved accuracy. Furthermore, our methods enable even efficient classifiers, such as ROCKET, to achieve better accuracy than using no channel selection or forward channel selection. To further study the impact of our techniques, we present experiments on classifying synthetic multivariate time series datasets with more than 100 channels, as well as a real-world case study on a dataset with 50 channels. Our channel selection methods lead to significant data reduction with preserved or improved accuracy.