Forecasting of Non-Stationary Sales Time Series Using Deep Learning

Autor:	Pavlyshenko, Bohdan M.
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.11636 Zobrazit plný text záznamu View this record from Arxiv