Predicting Television Ratings and Its Application to Taiwan Cable TV Channels

Abstract

Using forecast television network ratings, television executives estimate a price to sell time to advertisers. TV rating is an important feedback mechanism because its results greatly affect the immense profits of TV companies, advertisers, and program producers. Therefore, how to select the samples for TV rating investigation plays an important role in predicting program ratings. How to design an accurate predicting model for program rating also is an important investigation. The predicting problem is essentially a bi-objective optimization problem which minimizes the number of samples and maximizes the predicting accuracy of program rating. In this study, we propose an evolutionary approach to designing a rating model (ERM) by simultaneous optimization of sampling sub-area selection and parameter tuning using an intelligent genetic algorithm (IGA). In this study, the ERM is applied to Taiwan Cable TV Channels in Taipei and Taiwan. The experiments show that TV rating prediction of the proposed ERM is efficient smaller than that of using the same number of sub-areas with the largest TV ratings and an optimal prediction program rating by using the selected sub-areas.