Regression Models for Forecasting the Need for Study Places in Educational Organizations
Abstract
This study examines key aspects and regression approaches to forecasting the demand for study places in preschool and school-level educational institutions. Based on an analysis of key factors - demographic cohorts with time lags, migration flows, residential construction, parental employment, economic indicators, and regulatory changes - a methodological framework is formulated for constructing various types of regression models: linear and generalized linear models, as well as regularized and hybrid approaches with elements of machine learning. Particular attention is paid to the problems of lag data structure, spatial heterogeneity, small samples at the level of individual institutions, and the need for probabilistic forecasts. Practical procedures for data preprocessing, regressor selection, validation (temporal cross-validation, backtesting), and forecast quality assessment (MAPE, RMSE, CRPS), as well as scenario modeling, are proposed. This article is intended for education analysts and researchers seeking reliable and explainable tools for assessing space needs and making decisions on infrastructure resource allocation.
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