status: Published

In many time series classification tasks, each instance is described by multiple channels (i.e., signals). This introduces an additional computational burden as the time and resources to train a classifier increase as more channels become available. This is problematic as some tasks can be described by a huge number of channels. However, not all channels may be necessary as some could be irrelevant or redundant, and including these can (dramatically) increase run time while yielding no benefit in terms of predictive performance. Therefore, it can be useful to automatically select a subset of the channels to include in the analysis. We propose TSelect, a novel scalable and classifier-agnostic approach that automatically selects a relevant and non-redundant subset of the channels for multivariate time series classification (MTSC). Experimentally, we show on a large benchmark suite that TSelect (1) eliminates on average 62% of the channels, (2) significantly improves a classifier’s run time without sacrificing predictive performance, and (3) outperforms the two state-of-the-art channel selectors (ECS and ECP) on the majority of the experiments.