Ensemble approach for time series analysis in demand forecasting: Ensemble learning
Open accessAbstract
Demand forecasting for replenishment is one of the main issue for retail industry in terms of optimizing stocks, minimizing costs and also for reducing stock out problem. Better forecasting for demands, means maximizing sales and result with more revenue and profit for retailers. An other critical result of the stock out problem is of course dissatisfied customers and customer churn effect to retailers as well. Customers, in general do not wish to buy an equivalent product from different brands instead of their routine selections. There are of course many parameters which affect very seriously forecasting accuracy of consumer demands. For instance; seasonality, promotional effects, social events, new trends, unexpected crisis, terrorism, changes on weather conditions, commercial behavior of competitors at the market etc. In this study, new heuristic approach for ensemble methodology has been proved. It has been implemented in SOK Market. It is one of Turkey's hard discount retail chain with 4000 stores and replenishes 1500 SKUs to stores via 22 regional distribution centers. The results of this approach and how to take benefits of the powerful common minded demand forecasting in time series forecasting analysis have been showed.
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ML may be able to mitigate overproducing and/or overstocking goods by improving demand forecasting.
“ML may be able to mitigate these issues of overproducing and/or overstocking goods by improving demand forecasting [12,797].”
From Tackling climate change with machine learning (2023) · cites this paper as [12]
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A comprehensive evaluation of ensemble learning for stock-market prediction
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Tackling climate change with machine learning
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- Authors
- Akyuz, A. O., Uysal, M., Bulbul, B. A., & Uysal, M. O.
- Affiliations
- OBASE, Research & Development Center, Istanbul, Turkey
- Dogus University, Department of Computer Science, DOU, Istanbul, Turkey
- ORCID
- Akyuz, A. Okay
- Venue
- 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 7-12
- OpenAlex
- W2744982178
- Cited by
- 38
- References
- 14
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 2 · 2023: 8 · 2022: 8 · 2021: 1 · 2020: 3 · 2019: 4 · 2018: 3
- FWCI
- 4.02
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- International Conference on Innovations in Intelligent Systems and Applications (INISTA) (publishedVersion)
- Dogus University Institutional Repository (Dogus University) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Social Sciences › Business, Management and Accounting › Management Information Systems › Supply Chain and Inventory Management
- Keywords
- demand forecasting, ensemble learning, stockouts, time series forecasting, seasonal demand, promotional effects
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