Design and Implementation of an Intelligent Analytical System for Forecasting Key Economic Indicators

Abstract

This article examines the design and implementation of an intelligent analytical system for forecasting key economic indicators within a global, data-rich environment. Traditional econometric models often struggle to capture nonlinear dynamics and rapidly shifting conditions, underscoring the need for more adaptive forecasting tools. Drawing on advancements in machine learning, deep neural networks, and intelligent decision support systems, the study proposes a modular forecasting architecture that integrates multi-source data, automated preprocessing, hybrid modeling strategies, and interactive decision-support interfaces. The system leverages both classical statistical models and contemporary AI techniques to improve predictive accuracy, enhance interpretability, and support scenario-based planning. Practical considerations—including data quality, computational requirements, model transparency, and the integration of human expertise—are discussed, along with emerging innovations such as transformer-based time-series models and hybrid AI–economics frameworks. The study concludes that intelligent analytical systems hold significant potential to transform economic forecasting by enabling more timely, data-driven, and resilient decision-making across policy and industry contexts.