Abstract
This paper presents a comprehensive study comparing three distinct models including Autoregressive Integrated Moving Average (<i>ARIMA</i>), Geometric Brownian Motion (<i>GBM</i>), and Artificial Neural Networks (<i>ANN</i>) for predicting Rwandan franc exchange rates in Eastern Africa countries. Utilizing historical exchange rate data from the National Bank of Rwanda (<i>BNR</i>), predictive models are constructed for each method. The analysis reveals that both the <i>ANN</i> and <i>ARIMA</i> outperform <i>GBM</i> in accurately approximating Rwandan franc exchange rates. These findings highlight the superior forecasting capabilities of <i>ARIMA</i> and <i>ANN</i> for Eastern African exchange rates, while providing insights into the performance of <i>GBM</i> in comparison.
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