Forecasting educational services in Kazakhstan using time series analysis methods: analysis of the growth in the number of children attending schools and universities
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Date
2026
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SDU University
Abstract
This dissertation develops a reproducible regional forecasting framework for demand indicators for education in Kazakhstan up to 2030. The study considers school enrolment, university enrolment, preschool capacity, technical and vocational education, population dynamics, birth rates and migration inflow at the level of individual administrative regions identified by KATO codes. The key research problem is to make forward-looking quantitative estimates for planning educational infrastructure, but demographic and migration processes vary greatly in the twenty regions of Kazakhstan. The methodology uses four families of forecasting models: Holt's linear trend, damped Holt trend, automatic non-seasonal ARIMA with orders selected by the corrected Akaike Information Criterion and ARIMAX dynamic regression models using lagged birth counts and migration inflow as exogenous demographic covariates. Model selection is performed for each region-indicator pair using three-year holdout cross-validation with mean absolute percentage error as evaluation metric. The empirical results confirm that there is not a single model dominating all regions and indicators. Damped exponential smoothing is effective for stable trend indicators, with ARIMAX specifications being more accurate in areas where predictive demographic covariates exist. Six-year lagged birth counts provide statistically significant leading indicators for school enrolment in several regions and migration in-flow helps explain urban enrolment pressure. The aggregate national forecasts indicate continued growth in total school enrolment and demand for preschool. There is strong regional heterogeneity in the rates of change. The scientific contribution ofthis work is a region-level comparative forecasting pipeline that includes the official demographic and educational statistics of Kazakhstan, uses uniform standards of model evaluation, and generates visual and tabular outputs for planning purposes. The practical contribution is a set of regional forecast grids, model aсcuracy heatmaps, demographic pipeline analyses and summary tables that can directly support school construction, teacher recruitment and higher education capacity allocation decisions.
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Keywords
educational demand, Kazakhstan, ARIMAХ, demographic indicators
Citation
Turapbay A / Forecasting educational services in Kazakhstan using time series analysis methods: analysis of the growth in the number of children attending schools and universities / SDU University / Department of Mathematics and Natural Sciences