id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-333490-8pv5x6tz	Liao, Yi	Early box office prediction in China’s film market based on a stacking fusion model	2020-10-06		.txt	text/plain	5979	309	56	Specifically, combining Extreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LightGBM) and k-Nearest Neighbor (KNN) algorithms, we establish a stacking model for box office prediction during a film's early stage of production (shooting period). (2015) added MPAA rating, competition, star value, sequels, and the number of screens to the prediction variables and proposed a pre-release box office prediction model based on a dynamic artificial neural network algorithm. Post-release prediction In addition to pre-release features, it also includes a large amount of theatre data, heat index, and audience comment information It contains the most information and the best predictive effectiveness, but the application value of the results is very low Next, we compare the contribute factors and the effectiveness of box office prediction at different stages (Table 1 ). Considering the availability of data and the predictive power of features, five pre-production factors are selected based on the film itself: genre, star value, release date, release area, and sequels.	./cache/cord-333490-8pv5x6tz.txt	./txt/cord-333490-8pv5x6tz.txt