Predicting Agricultural Growth in Jizzax Region Using Advanced Machine Learning Techniques: An ARIMA-Based Approach
Rajneesh Kler, Danish Ather, Gurinder Singh, Naina Chaudhary, Manik Arora
Abstract
In the republic of Uzbekistan, Jizzax is prominent region contributing to agriculture growth of the nation. The current study tries to predict the growth patterns of the region for appropriate policy interventions by the government. The data for the analysis is collected from the published government sources for 23 years spanning from year 2000 to 2023. The study utilizes the univariate time series models and validates the results by adopting ML techniques. The fitted According to the model, growth will trend upward for 2024–2028, in keeping with the objectives of regional development. The results demonstrate how machine learning may increase the accuracy of economic forecasts, which is crucial for Jizzax's agricultural planning and policymaking. Future studies in agricultural economics and the use of state-of-the-art ML and DL models in regional economic forecasting are made possible by this work.