CNN-VSR: A Deep Learning Architecture with Validation-Based Stopping Rule for Time Series Classication

Abstract

Deep learning methods for univariate time series classification (TSC) are recently gaining attention. Especially, convolutional neural network (CNN) is utilized to solve the problem of predicting class labels of time series obtained through various important applications, such as engineering, biomedical, and finance. In this work, a novel CNN model is proposed with validation-based stopping rule (VSR) named as CNN-VSR, for univariate TSC using 2-D convolution operation, inspired by image processing properties. For this, first, we develop a novel 2-D transformation approach to convert 1-D time series of any length to 2-D matrix automatically without any manual preprocessing. The transformed time series will be given as an input to the proposed architecture. Further, the implicit and explicit regularization is applied, as time series signal is highly chaotic and prone to over-fitting with learning. Specifically, we define a VSR, which provides a set of parameters associated with a low validation set loss. Moreover, we also conduct a comparative empirical performance evaluation of the proposed CNN-VSR with the best available methods for individual benchmark datasets whose information are provided in a repository maintained by UCR and UEA. Our results reveal that proposed CNN-VSR advances the baseline methods by achieving higher performance accuracy. In addition, we demonstrate that the stopping rule considerably contributes to the classifying performance of the proposed CNN-VSR architecture. Furthermore, we also discuss the optimal model selection and study the effects of different factors on the performance of the proposed CNN-VSR.