  We introduce and detail an atypical neural network architecture, called time
elastic neural network (teNN), for multivariate time series classification. The
novelty compared to classical neural network architecture is that it explicitly
incorporates time warping ability, as well as a new way of considering
attention. In addition, this architecture is capable of learning a dropout
strategy, thus optimizing its own architecture.Behind the design of this
architecture, our overall objective is threefold: firstly, we are aiming at
improving the accuracy of instance based classification approaches that shows
quite good performances as far as enough training data is available. Secondly
we seek to reduce the computational complexity inherent to these methods to
improve their scalability. Ideally, we seek to find an acceptable balance
between these first two criteria. And finally, we seek to enhance the
explainability of the decision provided by this kind of neural architecture.The
experiment demonstrates that the stochastic gradient descent implemented to
train a teNN is quite effective. To the extent that the selection of some
critical meta-parameters is correct, convergence is generally smooth and
fast.While maintaining good accuracy, we get a drastic gain in scalability by
first reducing the required number of reference time series, i.e. the number of
teNN cells required. Secondly, we demonstrate that, during the training
process, the teNN succeeds in reducing the number of neurons required within
each cell. Finally, we show that the analysis of the activation and attention
matrices as well as the reference time series after training provides relevant
information to interpret and explain the classification results.The comparative
study that we have carried out and which concerns around thirty diverse and
multivariate datasets shows that the teNN obtains results comparable to those
of the state of the art, in particular similar to those of a network mixing
LSTM and CNN architectures for example.
