Intelligent hybrid forecasting for Iraq exports

Abstract

Accurate forecasting of export trajectories is vital for countries to develop effective trade policies, assess economic growth opportunities, and make informed strategic decisions. This is particularly crucial for Iraq, a nation whose fiscal stability is deeply intertwined with its export performance. Recognizing the need for more sophisticated predictive methods in this domain, this research introduces an innovative hybrid model that synergizes Artificial Neural Networks (ANN) and Wavelet Transforms (WT). The integration of these two methodologies aims to enhance the precision and adaptability of forecasts of Iraq's export trends. By leveraging the individual strengths of ANN and WT, this model promises to offer a more robust and reliable tool for forecasting, catering to the dynamic and complex nature of export data. This study not only contributes to the theoretical framework of export prediction but also provides practical insights for policymakers and stakeholders in shaping future-oriented trade strategies.