머신러닝을 이용한 관중 수요 예측에 관한 연구
Study on Prediction of Attendance Using Machine Learning
0
조회
0
다운로드
7
인용
0
참고문헌
키워드
초록
특정한 이벤트나 콘텐츠를 즐기기 위해 모인 사람들을 관중 또는 관객이라고 하고, 모임의 특성에 따라 다양한 성향을 나타낸다. 그러한 차이점은 있지만, 일반적으로 관중 수는 경영적인 측면과 직결되는 요소로써, 관람료부터 다른 시설의 이용료등 다양한 수입을 통해 콘텐츠 판매를 위한 안정적인 재정 운영을 가능케 한다. 따라서 관중 수에 대한 예측은 마케팅과 예산 전략 수립에 주요한 요소로 활용될 수 있다. 본 연구에서는 관중 수에 대한 예측을 위한 여러 가지 기존 모델을 검토하고,그 중에서 효율적인 머신러닝 모델을 제안하고자 한다. 또한 딥러닝과 랜덤포레스트 모델을 혼용하여 일별 관중 수 예측과비정상적 관중 수 예측에 대한 연구를 진행하였다.
Abstract
People who gathered to enjoy a specific event or content are called audiences or spectators, and show variouspropensity according to the characteristics of the crowd. Although there is such a difference, in general, the numberof attendance is directly related to the business aspect, which enables stable financial operation for the sale of contentsthrough various incomes, such as the admission fee and the use of other facilities. Therefore, prediction of audiencecan be used as a major factor in marketing and budgeting strategies. In this study, we review several existing modelsfor predicting the number of attendance and propose an efficient machine learning model. In addition, we studied dailyattendance prediction and abnormal attendance prediction using combine DNN(Deep Neural Network) and RF(RandomForest) model.
참고문헌 (15)
- Richard Giulianotti, "Sport and Social Theorists" (2004), Palgrave Macmillan
- George E. P. Box, "Time Series Analysis: Forecasting and Control" (2015), Wiley
- A.K. Jain, "Artificial neural networks: a tutorial", Computer, 29(3), pp.31~44 (1996), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- B. Karlik, "Performance analysis of various activation functions in generalized MLP architectures of neural networks", International Journal of Artificial Intelligence and Expert Systems, 1(4), pp.111~122 (2011)
- Wann, D. L., "Social connections at sporting events : Attendance and its positive relationship with state social psychological well-being", North American Journal of Psychology, 10(2), pp.229~238 (2008)
- T. Hegazy, "Developing Practical Neural Network Applications Using Back-Propagation", Computer-Aided Civil and Infrastructure Engineering, 9(2), pp.145~159 (1994), Wiley[DOI]
- H. Larochelle, "An empirical evaluation of deep architectures on problems with many factors of variation", Proceedings of the 24th International Conference on Machine learning (2007), ACM
- A. Y. Ng, "Feature selection, L1 vs. L2regularization, and rotational invariance", Proceedings of the 21st International Conference on Machine learning (2004)
- R. J. Hyndman, "Another look at measures of forecast accuracy", International Journal of Forecasting, 22(4), pp.679~688 (2006)[DOI]
- Tamás D. Gedeon, "Data Mining of Inputs: Analysing Magnitude and Functional Measures", International Journal of Neural Systems, 08(02), pp.209~218 (2011), World Scientific Pub Co Pte Lt[DOI]
- 박진욱, "인공 신경망을 이용한 한국프로야구 관중 수요 예측에 관한 연구", 정보처리학회 논문지, 6(12), pp.565~572 (2017), 한국정보처리학회[DOI]
- "Korea Baseball Organization"[URL]
- "KT Wiz"[URL]
- Fausett, L. V., "Fundamental of neural networks:architectures, algorithms, & applications" (1994), Prentice-Hall
- Haykin, S. C., "Neural networks: A comprehensive foundation" (1994), Prentice-Hall