Abstract
The study is concerned with the problem of classification of multivariate time series using convolutional neural networks (CNNs). As CNNs regard inputs in the form of images, an original image-like format of temporal data is proposed. Along this line, several design alternatives are studied by forming images with the two corresponding coordinates built by the original temporal data and their differences and second differences. An overall design process is presented with a focus on investigating time series-image transformations. Experimental studies involving publicly available data sets are reported, along with a slew of comparative analyses.
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