Abstract: Methods and systems for prediction audience ratings are disclosed. A database of television (TV) viewing data may include program records for a multiplicity of existing TV programs. A system may receive a training plurality of program records from the TV viewing data, and for each program record a most similar TV program based on content characteristics may be identified. A synthetic program record may be constructed by merging features of each record and its most similar record. Audience performance metrics may be omitted from synthetic records. An aggregate of the training plurality of program records and the synthetic program records may be used to train a machine-learning (ML) model to predict audience performance metrics of the new or hypothetical TV programs not yet available for viewing and/or not yet transmitted or streamed.

Abstract: Methods, apparatus, and articles of manufacture are disclosed. An example apparatus includes a co-viewing calculator to calculate a co-viewing factor corresponding to a demographic of panelists based on exposure information, an exposure time calculator to calculate a number of census co-viewer exposure minutes based on the co-viewing factor, a data aggregator to determine an aggregate exposure time total based on census exposure information and the number of census co-viewer exposure minutes, and a report generator to generate a report including the aggregate exposure time total.