A review of artificial intelligence based demand forecasting techniques
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 정혜린 | - |
dc.contributor.author | 임창원 | - |
dc.date.available | 2020-04-14T02:21:27Z | - |
dc.date.issued | 2019-12 | - |
dc.identifier.issn | 1225-066X | - |
dc.identifier.issn | 2383-5818 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/38364 | - |
dc.description.abstract | Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field. | - |
dc.format.extent | 41 | - |
dc.language | 한국어 | - |
dc.language.iso | KOR | - |
dc.publisher | 한국통계학회 | - |
dc.title | A review of artificial intelligence based demand forecasting techniques | - |
dc.title.alternative | 인공지능 기반 수요예측 기법의 리뷰 | - |
dc.type | Article | - |
dc.identifier.doi | 10.5351/KJAS.2019.32.6.795 | - |
dc.identifier.bibliographicCitation | 응용통계연구, v.32, no.6, pp 795 - 835 | - |
dc.identifier.kciid | ART002547218 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000531009700002 | - |
dc.citation.endPage | 835 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 795 | - |
dc.citation.title | 응용통계연구 | - |
dc.citation.volume | 32 | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | big data | - |
dc.subject.keywordAuthor | artificial intelligence | - |
dc.subject.keywordAuthor | demand forecasting | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | 빅데이터 | - |
dc.subject.keywordAuthor | 인공지능 | - |
dc.subject.keywordAuthor | 수요예측 | - |
dc.subject.keywordAuthor | 머신러닝 | - |
dc.subject.keywordAuthor | 딥 러닝 | - |
dc.subject.keywordPlus | EXTREME LEARNING-MACHINE | - |
dc.subject.keywordPlus | NEURAL-NETWORK MODEL | - |
dc.subject.keywordPlus | ELECTRICITY CONSUMPTION | - |
dc.subject.keywordPlus | INTERMITTENT DEMAND | - |
dc.subject.keywordPlus | GENETIC ALGORITHMS | - |
dc.subject.keywordPlus | REGRESSION-MODEL | - |
dc.subject.keywordPlus | SUPPLY CHAINS | - |
dc.subject.keywordPlus | TIME-SERIES | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.description.journalRegisteredClass | esci | - |
dc.description.journalRegisteredClass | kci | - |
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