A deep graph kernel-based time series classification algorithm

Abstract

Time series data are sequences of values that are obtained by sampling a signal at a fixed frequency, and time series classification algorithms distinguish time series into different categories. Among many time series classification algorithms, subseries-based algorithms have received widespread attention because of their high accuracy and low computational complexity. However, subseries-based algorithms consider the similarity of subseries only by shape and ignore semantic similarity. Therefore, the purpose of this paper is to determine how to solve the problem that subseries-based time series classification algorithms ignore the semantic similarity between subseries. To address this issue, we introduce the deep graph kernel technique to capture the semantic similarity between subseries. To verify the performance of the method, we test the proposed algorithm on publicly available datasets from the UCR repository and the experimental results prove that the deep graph kernel has an important role in enhancing the accuracy of the algorithm and that the proposed algorithm performs quite well in terms of accuracy and has a considerable advantage over other representative algorithms.