Adacket: ADAptive Convolutional KErnel Transform for Multivariate Time Series Classification

Abstract

While existing multivariate time series classification (MTSC) methods using massive convolutional kernels show promise, they are resource-intensive and also rely on the trial and error design of convolutional kernels, limiting comprehensive design space exploration. This hinders fully exploiting convolutional kernels for feature extraction from multivariate time series (MTS) data. To address this issue, we propose a novel method called Adaptive Convolutional Kernel Transform (Adacket) to automatically design efficient 1D dilated convolutional kernels for various MTSC scenarios. Adacket formulates the design problem as a multi-objective optimization problem, with a focus on performance and resource efficiency jointly. It introduces a reinforcement learning agent to adaptively determine convolutional kernels in a sequential decision-making manner, and creates multi-action spaces to support comprehensive search in both the channel and time dimensions. By exploring the maximum value of multi-objective rewards within continuous action spaces, Adacket achieves high granularity establishment of convolutional kernels. Empirical evaluations on public UEA archives demonstrate that Adacket outperforms other advanced MTSC baselines, while providing a deeper understanding of its design selections.