Multivariate Time Series Classification using Dilated Convolutional Neural Network

General approach for time series classification is splitting time series to equal size Multivariate time series classification is a high segments using a fixed-length sliding window and extracting value and well-known problem in machine learning handcrafted features from the segments for classification community. Feature extraction is a main step tasks. The features are usually statistical measurements or in classification tasks. Traditional approaches employ features extracted from another domain such Fourier and handcrafted features for classification while Wavelet domain (Jiang & Yin, 2015; Ravi et al., 2017; Lin convolutional neural networks (CNN) are able et al., 2003). In multivariate time series classification, commonly, to extract features automatically. In this paper, information is extracted separately from each variate, we use dilated convolutional neural network for and the features are concatenated for the classification task multivariate time series classification.