FORECASTING KEY INDICATORS OF POPULATION LIVINGSTANDARDS IN REGIONS USING ARIMA MODELS

Abstract

The assessment and forecasting of population living standards are essential for designing effective socio-economic policies. This study focuses on the application of ARIMA (AutoRegressive Integrated Moving Average) models to forecast key indicators of living standards in regions. By utilizing time-series data, the research demonstrates how ARIMA models can capture trends, seasonality, and fluctuations in socio-economic indicators such as income, employment, and consumption. The results highlight the reliability of ARIMA-based forecasts in supporting regional development strategies and improving policy planning.