Forecasting of Non-Stationary Sales Time Series Using Deep Learning

• URL: http://arxiv.org/abs/2205.11636v1
• Date: Mon, 23 May 2022 21:06:27 GMT
• Title: Forecasting of Non-Stationary Sales Time Series Using Deep Learning
• Authors: Bohdan M. Pavlyshenko
• Abstract summary: The paper describes the deep learning approach for forecasting non-stationary time series with using time trend correction in a neural network model. The results show that the forecasting accuracy can be essentially improved for non-stationary sales with time trends using the trend correction block.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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