Advanced Machine Learning Techniques with Self-Attention Mechanisms for Enhanced Macroeconomic Trend Forecasting in BRICS Nations

Accurate forecasting of macroeconomic trends is crucial for informed policy-making in the dynamic economies of BRICS nations; yet, traditional methods struggle to capture complex temporal dynamics, such as non-linear relationships and long-range dependencies. This paper proposes a novel hybrid framework that integrates transformer architectures with ARIMA models to effectively forecast macroeconomic trends. By combining transformers to capture long-term dependencies with ARIMA for short-term temporal patterns, our hybrid approach offers a comprehensive and robust forecasting solution. The model incorporates domain-specific economic knowledge, such as the impacts of fiscal policy on inflation, to enhance accuracy. Evaluated on a comprehensive dataset of key economic indicators from BRICS countries, empirical results indicate significant improvements in forecasting accuracy over traditional models, demonstrating the effectiveness of the proposed hybrid approach. Additionally, an attention analysis was conducted to understand the internal mechanisms of the model, providing insights into the importance of various economic indicators over time and enhancing model transparency and interpretability. This research contributes to economic analysis by presenting a robust methodology that integrates domain expertise with advanced machine learning techniques, thereby improving macroeconomic forecasting accuracy in emerging markets.