A new multi-process collaborative architecture for time series classification

Abstract

Abstract Time series classification (TSC) is the problem of categorizing time series data by using machine learning techniques. Its applications vary from cybersecurity and health care to remote sensing and human activity recognition. In this paper, we propose a novel multi-process collaborative architecture for TSC. The propositioned method amalgamates multi-head convolutional neural networks and capsule mechanism. In addition to the discovery of the temporal relationship within time series data, our approach derives better feature extraction with different scaled capsule routings and enhances representation learning. Unlike the original CapsNet, our proposed approach does not need to reconstruct to increase the accuracy of the model. We examine our proposed method through a set of experiments running on the domain-agnostic TSC benchmark datasets from the UCR Time Series Archive. The results show that, compared to a number of recently developed and currently used algorithms, we achieve 36 best accuracies out of 128 datasets. The accuracy analysis of the proposed approach demonstrates its significance in TSC by offering very high classification confidence with the potential of making inroads into plentiful future applications.