A Study on Prediction of Attendance in Korean Baseball League Using Artificial Neural Network

KIPS Transactions on Software and Data Engineering, Vol. 6, No.12, pp.565-572, December 2017
10.3745/KTSDE.2017.6.12.565, Full Text

Abstract

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.


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Cite this paper

[KIPS Transactions Style]
J. Park and S. Park, "A Study on Prediction of Attendance in Korean Baseball League Using Artificial Neural Network," KIPS Transactions on Software and Data Engineering, Vol.6, No.12, pp.565-572, 2017, DOI: 10.3745/KTSDE.2017.6.12.565.

[IEEE Style]
Jinuk Park and Sanghyun Park, "A Study on Prediction of Attendance in Korean Baseball League Using Artificial Neural Network," KIPS Transactions on Software and Data Engineering, vol. 6, no. 12, pp. 565-572, 2017. DOI: 10.3745/KTSDE.2017.6.12.565.

[ACM Style]
Park, J. and Park, S. 2017. A Study on Prediction of Attendance in Korean Baseball League Using Artificial Neural Network. KIPS Transactions on Software and Data Engineering, 6, 12, (2017), 565-572. DOI: 10.3745/KTSDE.2017.6.12.565.