Optimized FOREX Rate Prediction Using Hybrid Machine Learning Algorithm

This research examines the application of fuzzy time series (FTS) models and different machine learning techniques to anticipate changes in foreign exchange (FOREX) data. The statistics used in this investigation include the actual FOREX prices and the high and low values of four different currencies. These statistics begin from January 2012 to November 2022; the data is available on the Reserve Bank of India website. This article investigates the significant flaws present in the literature information on FTS. This article identifies fundamental flaws in all FTS-based algorithms presently available in the literature and shows how to fix them. The study then describes the unique structure of improvised FTS. The Hybrid Machine Learning Algorithm is the name given to this structure. The purpose is to solve the identified issues present in FTS-based models previously published in the literature. The result was compared with the before and after optimization processes. After implementing the proposed hybrid machine learning algorithm, the result shows the better accuracy.