Goloburda, Maiya2024-05-032024-05-032024-04-15Goloburda, M. (2024). TIME SERIES FORECASTING METHODS FOR SOCIO-ECONOMIC INDICATORS: A CASE STUDY OF KAZAKHSTAN. Nazarbayev University School of Sciences and Humanitieshttp://nur.nu.edu.kz/handle/123456789/7620This study compares traditional statistical methods (ARIMA, ETS) with LSTM, a deep learning approach, to forecast key socio-economic indicators (GDP, Population Growth, Price Index, Income per Capita, Housing prices) in Kazakhstan. Using historical data from the Bureau of National Statistics, the models are trained and evaluated using metrics MAE, MAPE and RMSPE. The research aims to understand the strengths and limitations of each method in the context of Kazakhstan's socio-economic data, providing insights for future forecasting in the region.enAttribution-NonCommercial-NoDerivs 3.0 United StatesType of access: RestrictedTime Series ForecastingARIMAETSLSTMSocio-Economic IndicatorsCapstone Project