Abstract:
In recent time, there is an increasing growth in the amount of trading taking place in the currency exchange market. However, effective analysis and simulation tools for performing accurate prediction of these exchange rates are lacking. To alleviate this challenge, this work proposes a simple but promising hybrid machine learning and prediction model by suitable combining the Sample Mean Estimator (SME) simulation architecture within the multiple linear regression technique based training of feed-forward parameters. The study presents the design and implementation of an automated currency exchange forecasting using Sample Mean Estimator (SME), Multiple linear regression machine learning model. The proposed model has the capability to overcome prediction inaccuracy, inconsistent forecasting, slow response due to computational complexity and scalability problems. This work also showcases the development of a novel simulation method referred to as Sample Mean Estimator (SME) simulation method. This SME simulation method is used for simulating the values of the dependent variables for the proposed predictive model. The SME method is used to overcome the problems of uncertainty and non-linearity nature of the predictive variable as it’s always affected by economic and political factors. The implementation of the proposed currency exchange rate forecasting system is achieved through the use of a developed in-house Java program with NetBeans as the editor and compiler.In designing the automated forecasting system, the process of data mart creation and model extraction was implemented using database management software specifically the Structured Query Language, MySQL. Performance comparison between the proposed system and two baseline methods which are the Autoregressive Moving Average and the Deep Belief network techniques demonstrates that the proposed forecasting model out-performed the baseline methods studies.
