  Analyzing multivariate time series data is important for many applications
such as automated control, fault diagnosis and anomaly detection. One of the
key challenges is to learn latent features automatically from dynamically
changing multivariate input. In visual recognition tasks, convolutional neural
networks (CNNs) have been successful to learn generalized feature extractors
with shared parameters over the spatial domain. However, when high-dimensional
multivariate time series is given, designing an appropriate CNN model structure
becomes challenging because the kernels may need to be extended through the
full dimension of the input volume. To address this issue, we present two
structure learning algorithms for deep CNN models. Our algorithms exploit the
covariance structure over multiple time series to partition input volume into
groups. The first algorithm learns the group CNN structures explicitly by
clustering individual input sequences. The second algorithm learns the group
CNN structures implicitly from the error backpropagation. In experiments with
two real-world datasets, we demonstrate that our group CNNs outperform existing
CNN based regression methods.
