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Comparative Time Series Analysis: The case of Emigration in Portugal
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Resumo(s)
This thesis explores emigration dynamics in Portugal through a quantitative time series framework, with a primary focus on comparing the forecasting performance of classical statistical models and modern foundational models. Using annual data, the study examines the relationship between emigration flows and selected socio-economic and demographic variables, namely unemployment rate, education, Gross Domestic Product (GDP) and fertility index. Classical approaches including Autoregressive Integrated Moving Average with eXogenous variables (ARIMAX) models and Generalized Additive Models (GAM), are benchmarked against a deep learning-based foundational model, the Temporal Fusion Transformer (TFT). The models are evaluated in terms of their ability to capture temporal dependencies, non-linear effects, and multivariate interactions: forecasting performance is assessed using standard error metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The comparative analysis reveals differences in predictive accuracy between classical models and the TFT, highlighting the strengths and limitations of each modelling approach in the context of emigration forecasting. Overall, the findings contribute to a better understanding of the socio-economic drivers of emigration and demonstrate the potential of foundational models for demographic and migration forecasting, offering relevant insights for policy analysis and sustainable development planning.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
Palavras-chave
Time series Emigration Auxiliar variables Forecasting Accuracy Metrics