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Machine-Learning Models for Sales Time Series Forecasting

1
SoftServe, Inc., 2D Sadova St., 79021 Lviv, Ukraine
2
Ivan Franko National University of Lviv, 1, Universytetska St., 79000 Lviv, Ukraine
This paper is an extended version of conference paper: Bohdan Pavlyshenko. Using Stacking Approaches for Machine Learning Models. In Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, 21–25 August 2018.
Received: 3 November 2018 / Revised: 9 January 2019 / Accepted: 14 January 2019 / Published: 18 January 2019
(This article belongs to the Special Issue Data Stream Mining and Processing)
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Abstract

In this paper, we study the usage of machine-learning models for sales predictive analytics. The main goal of this paper is to consider main approaches and case studies of using machine learning for sales forecasting. The effect of machine-learning generalization has been considered. This effect can be used to make sales predictions when there is a small amount of historical data for specific sales time series in the case when a new product or store is launched. A stacking approach for building regression ensemble of single models has been studied. The results show that using stacking techniques, we can improve the performance of predictive models for sales time series forecasting. View Full-Text
Keywords: machine learning; stacking; forecasting; regression; sales; time series machine learning; stacking; forecasting; regression; sales; time series
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Pavlyshenko, B.M. Machine-Learning Models for Sales Time Series Forecasting. Data 2019, 4, 15.

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