  Time series modelling is essential for solving tasks such as predictive
maintenance, quality control and optimisation. Deep learning is widely used for
solving such problems. When managing complex manufacturing process with neural
networks, engineers need to know why machine learning model made specific
decision and what are possible outcomes of following model recommendation. In
this paper we develop framework for capturing and explaining temporal
dependencies in time series data using deep neural networks and test it on
various synthetic and real world datasets.
