基于加权K-Means和局部BPNN的票房预测模型

Box-Office Forecasting Model Based on Weighted K-Means Clustering and Local BPNN

电影作为典型的短周期、体验型产品,其票房收益受众多因素的共同影响,因此对其票房进行预测较为困难.本文主要构建了一种基于加权K-均值以及局部BP神经网络(BPNN)的票房预测模型对目前的票房预测模型存在的不足进行改进,从而提高票房预测的精度:(1)构建基于随机森林的影响因素影响力测量模型,并以此为依据对票房影响因素进行筛选,以此来简化后续预测模型的输入;(2)考虑到不同影响因素对票房的影响力不同的现实情况,为了解决以往研究中对影响因素权重平均分配的问题,本文构建了基于加权K-均值和局部BP神经网络的票房预测模型,以因素影响力为依据对样本数据进行加权的K-均值聚类,并基于子样本构建局部BP神经网络模型进行票房预测.实验证明,本文所构建的模型平均绝对百分比误差(MAPE)为8.49%,低于对比实验的10.39%,可以看出本文构建的基于加权K-均值以及局部BP神经网络的票房预测模型的预测结果要优于对比模型的预测结果.

As a typical short cycle and experiential product, Movie’s box-office is influenced by many factors, so it is hard to forecast its box-office accurately. In this study, a box-office forecasting model based on weighted K-means and local BP Neural Network(BPNN) is constructed, with aims to improve the shortcomings of the current model and improve the accuracy of box office prediction:(1) Construct the factor influence measurement model based on Random Forest(RF)and simplify the box-office influence factors according to the value of variable importance, to achieve the purpose of simplifying the input of the following forecasting model.(2) In the traditional researches, the weight of each factor was equally allocated in sample classification, which without considering the question of different factor has different influence. So a box-office forecasting model based on weighted K-means and local BPNN is constructed, using weighted K-means clustering to classify the samples based on the value of factor influence, then build several local BPNN models based on each subsample. Experiments show that the Mean Absolute Percentage Error(MAPE) of this study’s model is8.49%, which is lower than 10.39% of the contrast experiment, which proves the superiority of the box-office forecasting model built in this study.