Pizza sales forecasting using big data analysis
- Authors
- Lee, Daebum; Kim, Juntae
- Issue Date
- May-2015
- Publisher
- International Information Institute Ltd.
- Keywords
- Big data mining; Data mining algorithms; Sales forecasting
- Citation
- Information (Japan), v.18, no.5, pp 1577 - 1584
- Pages
- 8
- Indexed
- SCOPUS
- Journal Title
- Information (Japan)
- Volume
- 18
- Number
- 5
- Start Page
- 1577
- End Page
- 1584
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/20033
- ISSN
- 1343-4500
- Abstract
- In today's business environments analysis of big data offers quite remarkable advantage for businesses over their competitors. This study gathers and analyzes big data to propose a pizza sales forecasting model. To that end, the past sales data, and data of events such as holidays, weather, news articles, economic indices, trends, and sports were gathered and used. Sales forecasting methods such as regression analysis and artificial neural network learning model were used to compare the accuracy of forecasting in the presence and absence of collected big data. Experiment results found that the forecasting error rate was improved by over 5% when using big-data.. ©2015 International Information Institute.
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Collections - College of Advanced Convergence Engineering > Department of Computer Science and Artificial Intelligence > 1. Journal Articles

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