TIME SERIES FORECASTING METHODS FOR SOCIO-ECONOMIC INDICATORS: A CASE STUDY OF KAZAKHSTAN

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dc.contributor.authorGoloburda, Maiya
dc.date.accessioned2024-05-03T11:13:12Z
dc.date.available2024-05-03T11:13:12Z
dc.date.issued2024-04-15
dc.description.abstractThis 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.en_US
dc.identifier.citationGoloburda, M. (2024). TIME SERIES FORECASTING METHODS FOR SOCIO-ECONOMIC INDICATORS: A CASE STUDY OF KAZAKHSTAN. Nazarbayev University School of Sciences and Humanitiesen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/7620
dc.language.isoenen_US
dc.publisherNazarbayev University School of Sciences and Humanitiesen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectType of access: Restricteden_US
dc.subjectTime Series Forecastingen_US
dc.subjectARIMAen_US
dc.subjectETSen_US
dc.subjectLSTMen_US
dc.subjectSocio-Economic Indicatorsen_US
dc.titleTIME SERIES FORECASTING METHODS FOR SOCIO-ECONOMIC INDICATORS: A CASE STUDY OF KAZAKHSTANen_US
dc.typeCapstone Projecten_US
workflow.import.sourcescience

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