Convolutional Neural Networks with Dynamic Convolution for Time Series Classification

Abstract

Due to its prominent applications, time series classification is one of the most important fields of machine learning. Although there are various approaches for time series classification, dynamic time warping (DTW) is generally considered to be a well-suited distance measure for time series. Therefore, in the early 2000s, techniques based on DTW dominated this field. On the other hand, deep learning techniques, especially convolutional neural networks (CNN) were shown to be able to solve time series classification tasks accurately. Although CNNs are extraordinarily popular, the scalar product in convolution only allows for rigid pattern matching. In this paper, we aim at combining the advantages of DTW and CNN by proposing the dynamic convolution operation and dynamic convolutional neural networks (DCNNs). The main idea behind dynamic convolution is to replace the dot product in convolution by DTW. We perform experiments on 10 publicly available real-world time-series datasets and demonstrate that our proposal leads to statistically significant improvement in terms of classification accuracy in various applications. In order to promote the use of DCNN, we made our implementation publicly available at https://github.com/kr7/DCNN.