• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.330-333
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• 2019

본 연구는 기존 관중 수 예측에 주로 사용되는 ARIMA 모형과 다른 GKFN(Network with Gaussian kernel functions) 모델을 시계열 모델로 제안하고 여러 변수 간의 상관관계를 분석한 MLP(Multilayer Perceptron) 모델을 각각 따로 만들어 두 가지 RMSE값의 가중치를 결합한 새로운 모델을 최종적으로 제안한다. GKFN 모델은 phase space 분석을 위해 smoothness measure를 측정하고 커널 개수를 늘려가며 학습시키는 방법이다. 또한, MLP 모델은 관중 수에 영향을 주는 여러 변수(날짜, 날씨 등 팀과 관련된 특징들)의 상관관계를 correlation coefficient 값을 이용해 분석하고 높은 상관관계를 가지는 변수들을 이용해 MLP 모델을 만들어 학습하는 것이다. 이를 통해 프로야구팀 기아 타이거즈의 일일 단위 관중 수를 예측하고자 하였다. 관중 수 예측을 통해 구단과 관객 모두 긍정적인 활용이 가능할 것이다. 훈련 자료는 2010년부터 2018년까지 9년 동안 기아 타이거즈의 일별 관중 수를 자료로 하였다.

• Journal of IKEEE
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• v.23 no.4
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• pp.1243-1249
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• 2019

People who gathered to enjoy a specific event or content are called audiences or spectators, and show various propensity according to the characteristics of the crowd. Although there is such a difference, in general, the number of attendance is directly related to the business aspect, which enables stable financial operation for the sale of contents through various incomes, such as the admission fee and the use of other facilities. Therefore, prediction of audience can be used as a major factor in marketing and budgeting strategies. In this study, we review several existing models for predicting the number of attendance and propose an efficient machine learning model. In addition, we studied daily attendance prediction and abnormal attendance prediction using combine DNN(Deep Neural Network) and RF(Random Forest) model.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.131-135
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• 2024

Baseball attracts the largest audience among professional sports in Korea. In particular, attendance is the primary source of income in baseball. Previous studies have limitations in reflecting the characteristics of individual stadium. For instance, the KIA Tigers exhibit the highest away game revenue among domestic teams, but they show lower home game earnings. Therefore, we aim to predict the daily attendance at the Gwangju-KIA Champions Field of the KIA Tigers using deep learning. We collected and preprocessed daily attendance, dates, weather, and team-related variables for Gwangju-KIA Champions Field from 2018 to 2023. We propose a deep learning-based linear regression model to predict the daily attendance. We expect that the proposed deep learning model will be used as basic information to increase the club's revenue.

• The Journal of the Korea Contents Association
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• v.8 no.10
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• pp.278-286
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• 2008

This study's purpose is to offer to basic data in each professional soccer team's spectator marketing strategy through researching the influence of spectators' life style on the appeal of professional soccer. This study's survey is target 500 spectators who were present at the professional soccer championship game in 2007 at Seongnam Stadium. Data analysis was done on 484 spectators's questionnaires using SPSS V. 12.0. To analyze materials the frequency, correlations, and regression analysis were used as statistic analysis techniques. The conclusion based on above study method and the result of material analysis are here below. First, the influence of spectator's life-style on the loyalty(action loyalty, attitude loyalty)of professional soccer. Second, the influence of spectator's life style on the appeal(field factor, game circumstance factor, spectating expense factor, promotion factor, entertainment factor, player factor)of professional soccer.

• Journal of Food Hygiene and Safety
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• v.15 no.1
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• pp.36-40
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• 2000

This study was carried out to know the active faction of Dryopteris rhizoma on antimicrobial activity against some food spoilage microorganisms. Also, antimicrobial activities were investigated for the aqueous and ethanolic extracts of four herbs such as Teminaliae fructus, Eugeniae flos, Salviae miltior-rhizae radix and Dryopteris rhizoma. Antimicrobial activities of three herbs except for Terminaliae fructus showed higher activities in 75% ethanolic extracts than in aqueous extract. Ethanolic extract of Dryopteris rhizoma showed the highest antimicrobial activity among extracts of four herbs. Antimicrobial activity intensities of solvent fractions of Dryopteris rhizoma extracted by 75% ethanol were order to CHC1$_3$fraction>EtOAc fraction >BuOH fraction>$H_2O$ fraction. The CHC1$_3$, EtOAc and BuOH fractions also inhibited growth of food spoilage microorganisms as the concentration increased, respectively. In case of EtOAc fraction, 1000 ppm of fraction almost inhibited completely the growth of microorganisms tested.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1041-1049
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• 2010

In Korean professional baseball, attendance is the largest source of revenue for development of professional baseball and the highest concern of professional baseball teams. So, if there is demand forecasting model, it will be helpful for pennant chasers to work out the strategies for drawing attendance. For this reason, this research intends to suggest the model which estimates Korean professional baseball's attendance and uses all usable variables which have an effect on attendance in limited circumstances. We supposed that dependent variable is attendance as well as several independent variables and error term are homoscedastic variance. And then, we compared the models which assume conditional heteroscedastic variance like GARCH and EGARCH with GARCH-t models which use the assumption that error term's distribution follows student-t distribution. In result of that, we could confirm that the models which were made by using GARCH(1,1)-t made estimates the most accurately among the several models considered.

• 전기산업
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• v.8 no.2
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• pp.58-74
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• 1997

• 전기산업
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• v.8 no.3
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• pp.68-77
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• 1997

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.920-923
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• 2017

본 연구는 기존의 수요 예측 등의 시계열 분석에서 주로 사용되는 ARIMA 모형의 어려움을 극복하고자 인공신경망(Artificial Neural Network) 모형을 이용하여 한국 프로 야구 관중 수를 예측하였다. 인공신경망의 가장 기본적인 종류인 전방향 신경망(Feedforward Neural Network)의 초모수(Hyperparameter) 선정에 그리드 탐색(Grid Search)을 적용하여 최적의 모형을 찾고자 하였다. 훈련 자료로는 2015년 3월부터 8월까지의 일별 KBO 관중 수 자료를 대상으로 하였고, 예측력 검증을 위해 2015년 9월 관중 수를 예측하여 실제 관측값과 비교하였다. 그 결과, 그리드 탐색법에서 최적 모형이라고 판단한 모형의 예측력은, 평균 절대 백분율 오차(MAPE) 기준으로 평균 27.14% 였다. 또한, 앙상블 기법에서 착안하여 오차율이 낮은 모형 5개의 예측값 평균의 MAPE는 평균 28.58% 였다. 이는 다중회귀와 비교해보았을 때, 평균적으로 각각 14%, 13.6% 높은 예측력을 보이고 있다.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.12
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• pp.565-572
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• 2017

Traditional method for time series analysis, autoregressive integrated moving average (ARIMA) allows to mine significant patterns from the past observations using autocorrelation and to forecast future sequences. However, Korean baseball games do not have regular intervals to analyze relationship among the past attendance observations. To address this issue, we propose artificial neural network (ANN) based attendance prediction model using various measures including performance, team characteristics and social influences. We optimized ANNs using grid search to construct optimal model for regression problem. The evaluation shows that the optimal and ensemble model outperform the baseline model, linear regression model.