A Machine Learning Predictive Model for Determining Daily Exchange Rate Movement in Sierra Leone

Abstract

"Prophet is an advanced machine learning tool designed for accurate time series forecasting. Utilizing a Bayesian additive regression model, it employs statistical techniques to analyze historical data and capture underlying patterns, trends, seasonality, and holiday effects. With its ability to handle uncertainties, anomalies, missing data, outliers, and changes in trends or seasonality, Prophet is a versatile solution for both univariate and multivariate time series analyses. In the context of the Sierra Leone currency market, our analysis using Prophet reveals valuable insights into the nominal exchange rates between the Leones and the dollar. On an annual basis, there is an observed upward trend in the nominal exchange rates. Weekly patterns indicate that the Leones tends to experience a slight depreciation on Tuesdays, while showing marginal stabilization or appreciation on Fridays. Additionally, the model highlights a tendency for marginal appreciation in the Leones from April to June, with a slight depreciation around September to October. These findings provide crucial information for risk management, economic planning, and decision-making in the Sierra Leone currency market. By understanding the identified trends in the Leones dollar exchange rates, stakeholders can make informed decisions regarding investments, currency trading, and overall economic strategies. This knowledge contributes to improving investor confidence and enables effective measures for mitigating risks. In summary, Prophet's Bayesian-based forecasting model offers probabilistic insights into future predictions, empowering decision-makers with accurate forecasts and valuable knowledge for strategic planning and risk management. "