论文标题
使用深度学习预测非平稳销售时间序列
Forecasting of Non-Stationary Sales Time Series Using Deep Learning
论文作者
论文摘要
本文描述了在神经网络模型中使用时间趋势校正的预测非平稳时间序列的深度学习方法。除了预测销售价值的层外，神经网络模型还包括一个时间趋势期限的预测权重块，该块被添加到预测的销售价值中。时间趋势项被认为是预测重量值和归一化时间值的产物。结果表明，在深度学习模型中，使用趋势校正块可以通过时间趋势来实质上提高预测准确性。
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.
