Forecasting box office performances using machine learning algorithms - Çağlıyor, S.; Öztayşi, B.; Sezgin, S. (Springer Verlag, 2020)
Motion picture industry is one of the largest industries worldwide and has a significant importance in the global economy. However, still each year, there is a considerable number of movies fail even to break even and lose a lot of money. Considering the high stakes and high risks in the industry forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study it is aimed to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey before their market entry. As independent variables MPAA rating, budget, star and director power, sequel, adaptation, number of screens, domestic performance, release time lag between domestic and foreign market are investigated. From sources like IMDB, Box Office Mojo, Box Office Türkiye a data set of 1585 movies is constructed and four models -Support Vector Regression (SVM), Artificial Neural Networks (ANN), Decision Tree Regression (DT) and Linear Regression (LR) are evaluated. Since our model is developed to predict the expected box office of a movie before its theatrical release in Turkey, it can help studios distributors and exhibitors in their decisions about market entry, timing of entry or distribution strategies. © 2020, Springer Nature Switzerland AG.
International Conference on Intelligent and Fuzzy Systems, INFUS 2019 -- 23 July 2019 through 25 July 2019 -- -- 228529
Keywords: Forecast Models; Machine Learning Algorithms; Motion Picture İndustry; Budget Control; Commerce; Decision Making; Decision Support Systems; Decision Trees; Forecasting; Learning Algorithms; Learning Systems; Machine Learning; Neural Networks; Regression Analysis; Support Vector Machines; Trees (Mathematics); Box-Office Performance; Decision Tree Regression; Distribution Strategies; Forecast Model; Global Economies; Independent Variables; Industry Forecasts; Support Vector Regression (Svr); Motion Pictures