Automated Prediction of Macroeconomic Index Shifts Using AI and ML Techniques

<jats:p>The automated prediction of macroeconomic index shifts has emerged as a critical research area with the integration of advanced Artificial Intelligence (AI) and Machine Learning (ML) methodologies.This study develops a robust analytical framework for forecasting variations in key macroeconomic indicatorssuch as Gross Domestic Product (GDP) growth, inflation dynamics, interest rate fluctuations, and stock market indices through the application of supervised, unsupervised, and deep learning techniques. The proposed approach incorporates multimodal time-series data, encompassing financial statements, global market sentiment, and policy-based variables, subjected to feature selection, dimensionality reduction, and ensemble modeling for enhanced accuracy and generalization.Comparative analyses of Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and transformer-based architectures reveal superior capabilities in modeling nonlinear dependencies and temporal interrelation.</jats:p>