Competition Analysis to Improve the Performance of Movie Box-Office Prediction

KIPS Transactions on Software and Data Engineering, Vol. 6, No.9, pp.437-444, September 2017
10.3745/KTSDE.2017.6.9.437, Full Text

Abstract

Although many studies tried to predict movie revenues in the last decade, the main focus is still to learn an efficient forecast model to fit the box-office revenues. However, the previous works lack the analysis about why the prediction errors occur, and no method is proposed to reduce the errors. In this paper, we consider the prediction error comes from the competition between the movies that are released in the same period. Our purpose is to analyze the competition value for a movie and to predict how much it will be affected by other competitors so as to improve the performance of movie box-office prediction. In order to predict the competition value, firstly, we classify its sign (positive/negative) and compute the probability of positive sign and the probability of negative sign. Secondly, we forecast the competition value by regression under the condition that its sign is positive and negative respectively. And finally, we calculate the expectation of competition value based on the probabilities and values. With the predicted competition, we can adjust the primal predicted box-office. Our experimental results show that predictive competition can help improve the performance of the forecast.


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Cite this paper

[KIPS Transactions Style]
G. He and S. Lee, "Competition Analysis to Improve the Performance of Movie Box-Office Prediction," KIPS Transactions on Software and Data Engineering, Vol.6, No.9, pp.437-444, 2017, DOI: 10.3745/KTSDE.2017.6.9.437.

[IEEE Style]
Guijia He and Soowon Lee, "Competition Analysis to Improve the Performance of Movie Box-Office Prediction," KIPS Transactions on Software and Data Engineering, vol. 6, no. 9, pp. 437-444, 2017. DOI: 10.3745/KTSDE.2017.6.9.437.

[ACM Style]
He, G. and Lee, S. 2017. Competition Analysis to Improve the Performance of Movie Box-Office Prediction. KIPS Transactions on Software and Data Engineering, 6, 9, (2017), 437-444. DOI: 10.3745/KTSDE.2017.6.9.437.