Cedi Appreciation Relative to Fuel Prices - A Machine Learning and Ancient Geomantic Approach

In recent years, the Ghanaian Cedi (GHS) has experienced notable appreciation against major global currencies, coinciding with fluctuating domestic fuel prices. Understanding the sustainability of this appreciation is crucial for policymakers, investors, and economic planners. This study adopts an innovative interdisciplinary approach by integrating Machine Learning (ML) techniques with principles from Ancient Geomancy, aiming to analyze and forecast the trajectory of the Cedi relative to fuel price dynamics. Quantitative analysis is conducted using historical exchange rate data and fuel pricing information, employing time series forecasting models such as Long Short-Term Memory (LSTM) networks, Random Forest Regression, and Prophet to predict future movements in the value of the Cedi. These models are evaluated using standard metrics including Root Mean Square Error (RMSE) and R-squared (R²). Complementing the ML analysis, we apply symbolic and spatial interpretations from Ancient Geomantic traditions particularly those relating to elemental balance and directional energy flows to provide a qualitative framework for interpreting economic cycles and currency stability. The integration of these two paradigms allows for a richer, multi-dimensional understanding of economic phenomena. Our findings suggest that while Machine Learning models offer robust predictive capabilities, Geomantic insights contribute contextual depth, potentially revealing underlying patterns not captured through conventional quantitative methods alone. This study contributes to the growing discourse on blending traditional knowledge systems with modern computational tools in financial and economic analysis.

Cedi appreciation, fuel prices, Machine Learning, Geomancy, economic forecasting, Ghanaian economy, LSTM, Random Forest, Prophet.

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