A Comparison of Elman Neural Network Performance Before and After Using Wavelet Transform

Abstract

Iraq's oil production is a critical component of its economy and a major contributor to global oil markets. As of recent data, the country produces approximately 4 million barrels per day (bpd), having adjusted its output to align with OPEC+ regulations. Historically, Iraq's production has fluctuated, influenced by internal factors like infrastructure development, political challenges, and global agreements. Over the period from 1992 to 2022, Iraq's oil production has demonstrated significant growth, with occasional dips during periods of conflict or global market adjustments. The country possesses vast reserves and has consistently worked on expanding its production capacity through partnerships and investments in key oil fields ELA HOMEPAGE.The analysis is based on  comparison between the Elman Neural Network before and after using wavelete transform to interpret the data. This study investigates the application of wavelet transforms in optimizing model performance, focusing specifically on reducing the mean square error (MSE). The analysis employed Daubechies wavelets at levels 1 and 2, with results demonstrating that the level 2 Daubechies wavelet outperformed others in minimizing MSE. The findings underscore the efficacy of wavelet transforms, particularly level 2 Daubechies, in enhancing the predictive accuracy of the model. These results highlight the significance of wavelet-based approaches in achieving improved performance metrics.