  Multivariate time series classification is a high value and well-known
problem in machine learning community. Feature extraction is a main step in
classification tasks. Traditional approaches employ hand-crafted features for
classification while convolutional neural networks (CNN) are able to extract
features automatically. In this paper, we use dilated convolutional neural
network for multivariate time series classification. To deploy dilated CNN, a
multivariate time series is transformed into an image-like style and stacks of
dilated and strided convolutions are applied to extract in and between features
of variates in time series simultaneously. We evaluate our model on two human
activity recognition time series, finding that the automatic features extracted
for the time series can be as effective as hand-crafted features.
