Master's thesis at the University of Basra examines the use of a hybrid model in forecasting with application
A master's thesis at the Faculty of Management and Economics at the University of Basra examined the use of the hybrid model ARFIMA-RBFN in forecasting with application.
The thesis submitted by the student (Mukhtar Hussein Mukhtar Al-Issa) aims that the hybrid model ARFIMA-RBFN combines the predictive power of the ARFIMA model in processing linear patterns and long memory, and the ability to capture non-linear patterns by the RBFN network, this combination provides a powerful and accurate tool for time series forecasting.
The thesis recommends the use of hybrid models such as ARFIMA-RBFN when analyzing complex time series, as these models can strike a balance between representing long-term effects and non-linear relationships.
Department of Media and Government Communication
