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http://dx.doi.org/10.9708/jksci.2020.25.06.035

Movie Box-office Prediction using Deep Learning and Feature Selection : Focusing on Multivariate Time Series  

Byun, Jun-Hyung (Dept. of Industrial Management Engineering, Korea University)
Kim, Ji-Ho (Dept. of Industrial Management Engineering, Korea University)
Choi, Young-Jin (Dept. of Industrial Management Engineering, Korea University)
Lee, Hong-Chul (Dept. of Industrial Management Engineering, Korea University)
Publication Information
Journal of the Korea Society of Computer and Information / v.25, no.6, 2020 , pp. 35-47 More about this Journal
Abstract
Box-office prediction is important to movie stakeholders. It is necessary to accurately predict box-office and select important variables. In this paper, we propose a multivariate time series classification and important variable selection method to improve accuracy of predicting the box-office. As a research method, we collected daily data from KOBIS and NAVER for South Korean movies, selected important variables using Random Forest and predicted multivariate time series using Deep Learning. Based on the Korean screen quota system, Deep Learning was used to compare the accuracy of box-office predictions on the 73rd day from movie release with the important variables and entire variables, and the results was tested whether they are statistically significant. As a Deep Learning model, Multi-Layer Perceptron, Fully Convolutional Neural Networks, and Residual Network were used. Among the Deep Learning models, the model using important variables and Residual Network had the highest prediction accuracy at 93%.
Keywords
Box-office Prediction; Feature Selection; Multivariate Time Series Classification; Random Forest; Deep Learning; Multi-Layer Perceptron; Fully Convolutional Neural Networks; Residual Network;
Citations & Related Records
Times Cited By KSCI : 18  (Citation Analysis)
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