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arXiv · 2205.11636

Forecasting of Non-Stationary Sales Time Series Using Deep Learning

Bohdan M. Pavlyshenko
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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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BibTeXRIS

Bohdan M. Pavlyshenko. 2022-05-23. Forecasting of Non-Stationary Sales Time Series Using Deep Learning. https://arxiv.org/abs/2205.11636

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