An Interval Reliability Demand Prediction Method Combined with XGBoost and D-S Evidence Theory in Film Preparation Period

Abstract

The lack of historical sales data and word-of-mouth information in the film preparation period, the few available variables and the uncertainty in the prediction process lead to the difficulty in predicting the total box office demand of films. To solve this problem, this paper constructed and verified the prediction method of interval reliability demand in the film preparation period, which combined XGBoost algorithm and D-S evidence theory. Firstly, the total box office interval was effectively divided according to the sample data of the training set, and XGBoost was used to complete the calculation of the reliability function value of the evidence variables. Then, the D-S evidence theory was used for information fusion to obtain the results of box office interval reliability fusion. Finally, the box office attribution was judged by the interval reliability, so as to realize the interval reliability demand prediction in the preparatory period. The validity of the proposed method was verified by selecting the data of Chinese films from 2017 to 2019, and it was compared with the classical predictive classification algorithm. The results showed that the method has higher prediction accuracy and better generalization ability.