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Machine-Learning Models for Sales Time Series Forecasting
| Content Provider | Directory of Open Access Journals (DOAJ) |
|---|---|
| Author | Bohdan M. Pavlyshenko |
| 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. |
| Related Links | http://www.mdpi.com/2306-5729/4/1/15 |
| DOI | 10.3390/data4010015 |
| Journal | Data |
| Issue Number | 1 |
| Volume Number | 4 |
| e-ISSN | 23065729 |
| Language | English |
| Publisher | MDPI AG |
| Publisher Date | 2019-01-01 |
| Publisher Place | Switzerland |
| Access Restriction | Open |
| Subject Keyword | Bibliography. Library Science. Information Resources Machine Learning Stacking Forecasting Regression Sales Time Series Science Bibliography. Library science. Information resources |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science Applications Information Systems Information Systems and Management |
