  Although deep networks have been widely adopted, one of their shortcomings
has been their blackbox nature. One particularly difficult problem in machine
learning is multivariate time series (MVTS) classification. MVTS data arise in
many applications and are becoming ever more pervasive due to explosive growth
of sensors and IoT devices. Here, we propose a novel network (IETNet) that
identifies the important channels in the classification decision for each
instance of inference. This feature also enables identification and removal of
non-predictive variables which would otherwise lead to overfit and/or
inaccurate model. IETNet is an end-to-end network that combines temporal
feature extraction, variable selection, and joint variable interaction into a
single learning framework. IETNet utilizes an 1D convolutions for temporal
features, a novel channel gate layer for variable-class assignment using an
attention layer to perform cross channel reasoning and perform classification
objective. To gain insight into the learned temporal features and channels, we
extract region of interest attention map along both time and channels. The
viability of this network is demonstrated through a multivariate time series
data from N body simulations and spacecraft sensor data.
