Forecasting Currency Exchange Rate Through a Hybrid Time-Series Analysis

Accurate exchange rate prediction plays a crucial role in promoting global economic stability and growth. Exchange rates being volatile have an impact on trade balance, inflation rates and monetary policies and have an impact on global economies as well as the domestic economy. This ability to analyze and predict such changes enables the policy-makers, economists, financial analysts and the national banks draw better policies. However, it is hard to forecast exchange rate because of the complexity of the financial systems and the overwhelming number of factors that constitutes an indicator to the exchange rates. Traditional models often struggle to capture non-linear relationship and high volatility prevailing into the financial data. Problems related to variability and the existence of a large number of economic indicators make analysis with these models less accurate. Moreover, they fail at incorporating macro-economic aspects and other market indicators effectively into your plans which leads to more often than not incomplete and sometimes even incorrect prognoses. To tackle these challenges, this research presents the F-CER (Forecasting Currency Exchange Rates) model which utilizes more advanced features like Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Multi-Head Attention, and Reinforcement learning (RL). By using the analysis of the historical rate of exchange of Chinese Yuan and US Dollar from 2000 to June 2023, and by incorporating relevant macroeconomic and market factors, F-CER successfully overcomes the shortcomings of the above models. The F-CER model applies advanced techniques for feature engineering as well as Principal Component Analysis and adaptive normalization to overcome the data dimensionality and variability issues. It finds significant shapes in noisy and massive time series and focuses on key variables that contribute to the enhanced accuracy of forecasts. Experimental results indicate that F-CER model perform well, and achieving a Mean Squared Error (MSE) of 0.005, Test Mean Absolute Error (MAE) of 0.089, Demonstrating its advanced predictive capabilities.