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- Predictive modeling of media audience based on time seriesPublication . Silva, Bruno Filipe Lopes da; Alves, Paulo; Fernandes, José EduardoThe rapid evolution of media consumption habits and the increasing competition between television and digital platforms have intensified the need for accurate audience forecasting. Understanding how audiences fluctuate over time is crucial for broadcasters, advertisers, and content producers seeking to optimize programming strategies and allocate resources efficiently. This dissertation presents a comprehensive study on the prediction of television audience ratings using machine learning and statistical models. The work compares multiple modelling approaches, including Linear Regression, Ridge Regression, Random Forest, Gradient Boosting (LightGBM), Long Short-Term Memory (LSTM) networks, and the SARIMA statistical model. The analysis was conducted on four datasets derived from Portuguese television audience data, covering pre- and post-COVID-19 periods and incorporating different program types schemes. It is important to emphasize that exclusively exogenous variables were used, that is, variables external to the audience generation process itself, deliberately excluding endogenous variables, in order to evaluate the predictive capacity of the models based only on contextual and programmatic factors. A rigorous preprocessing pipeline was implemented, including data cleaning, feature encoding, temporal normalization, and seasonality analysis. Hyperparameter optimization was performed using grid and randomized search methods, and models were evaluated according to MAE, RMSE, MSE, and R2 metrics. The results demonstrate that ensemble-based methods, particularly Random Forest and LightGBM, consistently outperform linear and statistical baselines, achieving R2 scores above 0.93. The LSTM network effectively captured temporal dependencies but showed sensitivity to the reduction of training data in the post-COVID subsets, while the SARIMA model proved less suitable for capturing nonlinear audience dynamics. The study also identifies clear evidence of seasonal and behavioural patterns in television audiences, which can be leveraged to improve future forecasting models. Future research directions include the integrating of external data sources such as social media and streaming platform metrics. Such extensions could further enhance the contextual understanding of audience behaviour and support data-driven decision-making in the broadcasting industry.