FORECASTING EXPORT AND IMPORT INDICATORS OF BUKHARA REGION
DOI:
https://doi.org/10.5281/zenodo.20711497Keywords:
ARIMA model; Box–Jenkins approach; time series analysis; export forecasting; import forecasting; Bukhara region; stationarity; ACF; PACF; foreign trade dynamics; econometric modeling.Abstract
This study analyzes and forecasts the export and import dynamics of Bukhara region for the
period 2016–2024 using quarterly time series data. The ARIMA (Autoregressive Integrated Moving Average)
modeling approach, based on the Box–Jenkins methodology, is applied to examine the behavior of foreign trade
indicators and to generate forecasts for 2025–2030. The stationarity of the time series is evaluated through
ACF and PACF analyses, confirming that the variables do not require seasonal adjustment and allowing the
use of non-seasonal ARIMA models. Model selection is performed using statistical criteria such as the Akaike
Information Criterion (AIC), Bayesian Information Criterion (BIC), and log-likelihood values. The results indicate
that the ARIMA(3,0,1) model is the most appropriate for export forecasting, while the ARIMA(4,0,1) model
provides the best fit for import dynamics. The forecast results show a steady increase in exports alongside
relatively higher and more volatile import levels, leading to a persistently negative trade balance over the
forecast horizon. These findings provide important insights for regional trade policy and economic planning in
Bukhara region.
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