Application of Functional Kernel Hypothesis Testing for Channel Selection in Time Series Classification

Abstract

Multi-variate time series classification tasks are prevalent in various real-world engineering domains, including but not limited to activity recognition and anomaly detection. However, due to the abundance of sensors available, selecting the appropriate channels for successful classification can be a daunting task. In this study, we propose to use a two-sample hypothesis test, to determine the relevance of channels in time series classification tasks. We illustrate the industrial usecase that motivated this algorithm and validate our approach on open-source benchmarks. The proposed method has the potential to address the challenge of channel selection in multi-variate time series classification tasks and can significantly impact various real-world engineering applications.