点击次数：

论文类型：期刊论文

发表时间：2009-04-01

发表刊物：EXPERT SYSTEMS WITH APPLICATIONS

收录刊物：SCIE、EI、Scopus

卷号：36

期号：3

页面范围：6580-6587

ISSN号：0957-4174

关键字：Movies; Box office revenue; Forecasting; BP neural network; MLP; Dynamic threshold; Performance analysis

摘要：Forecasting box office revenue of a movie before its theatrical release is a difficult and challenging problem. In this study, a multi-layer BP neural network (MLBP) with multi-input and multi-output is employed to build the prediction model. All the movies are divided into six categories ranged from "blob" to "bomb" according to their box office incomes, and the purpose is to predict a film into the right class. The selections of the input variables are based on market survey and their weight values are determined by using statistical method. As to the design of the neural network structure, theoretical guidance and plentiful experiments are combined to optimize the hidden layers' parameters which include the number of hidden layers and their node numbers. Then a classifier with dynamic thresholds is used to standardize the output for the first time, and it improves the robustness of the model to a high level. Finally, a 6-fold cross-validation experiment methodology is used to measure the performance of the prediction model. The comparison results with the MLP method show that the MLBP prediction model achieves more satisfactory results, and it is more reliable and effective to solve the problem. (C) 2008 Elsevier Ltd. All rights reserved.