Performance of Ensemble Methods for Economic Forecasting : Evaluating Bagging in Predicting House Price Index - Pilipas, Oleksandr (2025)
This thesis examines ensemble learning, particularly bagging, for forecasting the UK Housing Price Index (HPI) using quarterly data from 1968–2024, harmonized and second-order dif- ferenced for stationarity. A rolling expanding-window compares ARIMA, Bagged ARIMA, MLP, Bagged MLP, LSTM, and Random Forest across one- to four-quarter horizons using MAE and RMSE. Results show bagged neural nets markedly reduce variance and enable uncertainty quantification; ARIMA remains most accurate short-term, while deep ensembles improve longer-horizon predictions. The contribution includes a uniform evaluation frame- work and analysis of the accuracy–complexity–cost trade-off.