A deep learning framework for time series classification using Relative Position Matrix and Convolutional Neural Network

Abstract

Time series classification (TSC) which has attracted great attention in time series data mining task, has already applied to various fields. With the rapid development of Convolutional Neural Network (CNN), the CNN based methods on TSC have begun to emerge until recently. However, the performance of CNN based methods is slightly worse than state-of-the-art traditional methods. Therefore, we propose a novel deep learning framework using Relative Position Matrix and Convolutional Neural Network (RPMCNN) for the TSC task. We investigate a time series data representation method called Relative Position Matrix (RPM) to convert the raw time series data to 2D images which enable the use of techniques from image recognition. We also construct an improved CNN architecture to automatically learn a high-level abstract representation of low-level raw time series data. Therefore, the combination of RPM and CNN in a unified framework is expected to boost the accuracy and generalization ability of TSC. We conduct a comprehensive evaluation with various existing methods on a large number of standard datasets and demonstrate that our approach achieves remarkable results and outperforms the current best TSC approaches by a large margin.