Using Autoencoders and AutoDiff to Reconstruct Missing Variables in a
  Set of Time Series
Jan-Philipp Roche Oliver Niggemann Jens Friebe
Existing black box modeling approaches in machine learning suffer from a
fixed input and output feature combination. In this paper, a new approach to
reconstruct missing variables in a set of time series is presented. An
autoencoder is trained as usual with every feature on both sides and the neural
network parameters are fixed after this training. Then, the searched variables
are defined as missing variables at the autoencoder input and optimized via
automatic differentiation. This optimization is performed with respect to the
available features loss calculation. With this method, different input and
output feature combinations of the trained model can be realized by defining
the searched variables as missing variables and reconstructing them. The
combination can be changed without training the autoencoder again. The approach
is evaluated on the base of a strongly nonlinear electrical component. It is
working well for one of four variables missing and generally even for multiple
missing variables.