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Navigating Inflation in Ghana: How Can Machine Learning Enhance Economic Stability and Growth Strategies

Theophilus G. Baidoo, Ashley Obeng

TL;DR

Machine learning's potential to enhance Ghana's economic resilience and support sustainable development through effective inflation management is demonstrated, ultimately demonstrating ML's potential to enhance Ghana's economic resilience and support sustainable development through effective inflation management.

Abstract

Inflation remains a persistent challenge for many African countries. This research investigates the critical role of machine learning (ML) in understanding and managing inflation in Ghana, emphasizing its significance for the country's economic stability and growth. Utilizing a comprehensive dataset spanning from 2010 to 2022, the study aims to employ advanced ML models, particularly those adept in time series forecasting, to predict future inflation trends. The methodology is designed to provide accurate and reliable inflation forecasts, offering valuable insights for policymakers and advocating for a shift towards data-driven approaches in economic decision-making. This study aims to significantly advance the academic field of economic analysis by applying machine learning (ML) and offering practical guidance for integrating advanced technological tools into economic governance, ultimately demonstrating ML's potential to enhance Ghana's economic resilience and support sustainable development through effective inflation management.

Navigating Inflation in Ghana: How Can Machine Learning Enhance Economic Stability and Growth Strategies

TL;DR

Machine learning's potential to enhance Ghana's economic resilience and support sustainable development through effective inflation management is demonstrated, ultimately demonstrating ML's potential to enhance Ghana's economic resilience and support sustainable development through effective inflation management.

Abstract

Inflation remains a persistent challenge for many African countries. This research investigates the critical role of machine learning (ML) in understanding and managing inflation in Ghana, emphasizing its significance for the country's economic stability and growth. Utilizing a comprehensive dataset spanning from 2010 to 2022, the study aims to employ advanced ML models, particularly those adept in time series forecasting, to predict future inflation trends. The methodology is designed to provide accurate and reliable inflation forecasts, offering valuable insights for policymakers and advocating for a shift towards data-driven approaches in economic decision-making. This study aims to significantly advance the academic field of economic analysis by applying machine learning (ML) and offering practical guidance for integrating advanced technological tools into economic governance, ultimately demonstrating ML's potential to enhance Ghana's economic resilience and support sustainable development through effective inflation management.
Paper Structure (15 sections, 4 figures, 1 table)

This paper contains 15 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Review of Literature
  3. Methodology
  4. Source of Data
  5. Trend analysis of Inflation rates
  6. Stationarity
  7. Autocorrelation (ACF) and Partial Autocorrelation (PACF) Analysis
  8. Autoregressive Integrated Moving Average, ARIMA (p, d, q)
  9. Implementation of Recurrent Neural Networks (RNN) and Long Short-term Memory (LSTM)
  10. Results and Discussion
  11. ARIMA Model
  12. Forecasting
  13. RNN and LSTM
  14. Model Evaluation
  15. Conclusion and Recommendations

Figures (4)

  • Figure 1: Time series plot of Inflation in Ghana from 2010 to 2021
  • Figure 2: Time series plot of Inflation in Ghana from 2010 to 2021 after differencing
  • Figure 3: Forecasted Inflation rates for the Period 2022 - 2023 using ARIMA (1, 0, 1)
  • Figure 4: Left: Projections of Actual Inflation and RNN Predictions. Right: Projections of Actual Inflation and LSTM Predictions.