  The paper describes the deep learning approach for forecasting non-stationary
time series with using time trend correction in a neural network model. Along
with the layers for predicting sales values, the neural network model includes
a subnetwork block for the prediction weight for a time trend term which is
added to a predicted sales value. The time trend term is considered as a
product of the predicted weight value and normalized time value. The results
show that the forecasting accuracy can be essentially improved for
non-stationary sales with time trends using the trend correction block in the
deep learning model.
