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.

翻译：论文介绍了在神经网络模型中使用时间趋势校正模型预测非静止时间序列的深层次学习方法,与预测销售价值的层数一起,神经网络模型还包括一个用于预测时间趋势值的预测加权值的子网络块,并添加到预测销售值中。时间趋势值被视为预测重量值和正常时间值的产物。结果显示,对非静止销售的预测准确度可以基本提高,使用深层学习模型中的趋势校正块进行时间趋势调整。