A COMPARATIVE ANALYSIS OF TIME SERIES MODELS AND ARIMA ALGORITHMS FOR FORECASTING E-COMMERCE GROWTH IN UZBEKISTAN

Abstract

This study examines the application of Time Series models and ARIMA algorithms in forecasting the growth of e-commerce in Uzbekistan. With the rapid digital transformation of the national economy and the increasing adoption of online shopping platforms, accurate forecasting of e-commerce volume has become essential for strategic planning and decision-making. The research aims to compare the forecasting performance of traditional Time Series methods and the ARIMA model using historical e-commerce data. Various statistical indicators, including forecasting accuracy, model stability, and predictive reliability, are analyzed to evaluate the effectiveness of each approach. The findings demonstrate the strengths and limitations of both methodologies and provide insights into selecting the most suitable forecasting model for e-commerce market analysis in Uzbekistan. The study contributes to the development of data-driven decision-making processes and supports the sustainable growth of the digital economy.