Comparing performance of ensemble methods in predicting movie box office revenue

While many business intelligence methods have been applied to predict movie box office revenue, the studies using an ensemble approach to predict box office revenue are almost nonexistent. In this study, we propose decision trees, k-nearest-neighbors (k-NN), and linear regression using ensemble methods and the prediction performance of decision trees based on random forests, bagging and boosting are compared with that of k-NN and linear regression based on bagging and boosting using the sample of 1439 movies. The results indicate that ensemble methods based on decision trees (random forests, bagging, boosting) outperform ensemble methods based on k-NN (bagging, boosting) in predicting box office at week 1, 2, 3 after release. Decision trees using ensemble methods provide better prediction performance than ensemble methods based on linear regression analysis in the box office at week 1 after release. This is explained by the results that after comparing the prediction performance between ensemble methods and non-ensemble methods. For decision tree methods, unlike the other methods, the prediction performance of ensemble methods is greater than that of non-ensemble methods. This shows that decision trees using ensemble methods provide better application effectiveness of ensemble methods than k-NN and linear regression analysis.