Prediction of movie box office based on the Bayesian-Stacking model
This paper constructs a feature selection method based on XGBoost and a box office prediction model based on the Bayesian-Stacking integrated algorithm.Firstly,constructing XGBoost's influence measurement model to screen variables can simplify the input of the later model and improve the interpretability of the model's characteristic variables;Secondly,BP neural network,XGBoost,Logistic Regression,LightGBM,GBDT,and Stacking models are constructed respectively,and then the box office of the film during the release period is predicted after the global optimization of the above models is realized by Bayesian optimization algorithm.Finally,the evaluation index is introduced for analysis.The results show that:1)the Bayesian optimization algorithm is combined with the model,and higher prediction accuracy is obtained compared with the original model;2)the Bayesian-Stacking model is superior to other models in box office prediction accuracy.The Bayesian-Stacking model has a high reference value in predicting the final box office during the film release period and can provide decision-making reference for relevant departments.
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