Time Series Similarity Measure via Siamese Convolutional Neural Network

Abstract

The similarity measure is a fundamental and key problem for time series analysis, which is widely used in classification, cluster, motif discovery and etc. Time series similarity calculation has been studied a lot and many methods have been proposed, such as Euclidean distance and elastic distance measures. However, all these methods are distance-based which get the similarity by accumulating the distances between optimal match pairs and ignore the intrinsic differences of time series. In order to solve the drawback, we discard this distance-based technique and design a Siamese Convolutional Neural Network(SCNN) to obtain the similarity of time series in this paper. Concretely, we first extract the essential features for similarity calculation by Convolutional Neural Network and then ontain the absolute difference between the essential features of time series. To conveniently train the proposed model, we make full use of the label information of datasets and construct a new binary dataset which each example contains two original time series and a binary label. In the proposed Siamese network, the parameters are shared between two branches and we take binary cross-entropy function to train our model. Experimental results on synthetic and real time series datasets show that our SCNN model is effective and achieves improved accuracy for time series classification task compared to other time series similarity calculation methods.