Machine-Learning Models for Sales Time Series Forecasting

Bohdan M. Pavlyshenko
Additional contact information
Bohdan M. Pavlyshenko: SoftServe, Inc., 2D Sadova St., 79021 Lviv, Ukraine

Data, 2019, vol. 4, issue 1, 1-11

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.

Keywords: machine learning; stacking; forecasting; regression; sales; time series (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.mdpi.com/2306-5729/4/1/15/pdf (application/pdf)
https://www.mdpi.com/2306-5729/4/1/15/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:4:y:2019:i:1:p:15-:d:198898

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().