Abstract
TV advertising makes up more than one third of total ad spending, and is transacted based on forecast ratings and viewership. Over time, forecast accuracy has decreased due to fragmentation of consumer behavior. Through a comprehensive study we find that an assortment of models combined with an ensemble method leads to better accuracy than any single method. This results in an 11% improvement over a naive baseline method, across 100 of the largest networks.
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Acknowledgment
This work was supported in part by Pitney Bowes 3100041700, Alfred P. Sloan Foundation G-2018-11069, and NSF award 1827505.
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Khryashchev, D., Papiu, A., Xuan, J., Dinica, O., Hubert, K., Vo, H. (2019). Who Watches What: Forecasting Viewership for the Top 100 TV Networks. In: Tagarelli, A., Tong, H. (eds) Computational Data and Social Networks. CSoNet 2019. Lecture Notes in Computer Science(), vol 11917. Springer, Cham. https://doi.org/10.1007/978-3-030-34980-6_19
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DOI: https://doi.org/10.1007/978-3-030-34980-6_19
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