Film Box Office Forecasting Methods Based on Partial Least Squares Regression Model

Abstract

Every year, billions of films appear in the box office of the mainland but there is small statistics of sample data for them. There are numerous factors responsible for it e.g. complex, variable box office elements and low accuracy of box office demand forecasting. Whereas, partial least squares regression model has the capability to deal with small sample data and variable multiple correlations. This paper has conducted an empirical analysis by using 13 indexes affecting the movie box office to construct movie box-Office forecast model as well as analyze the principles and the construction steps of the models. The model has utility with respects to process and model accuracy. The empirical results show that the absolute relative error of the partial least squares regression model is 26.6%, the goodness of fit is 87.7%. It shows that the partial least squares model has great skills to demonstrate the prediction of results in accurate and fashioned way.