Exploration on use of time series analysis and machine learning to predict the population of countries and global trend

Abstract

This study focuses on the crucial task of forecasting population growth, a fundamental aspect of planning for a nation's future development. Utilizing advanced machine learning techniques, we aim to predict future population trends by analyzing historical demographic data. This approach is expected to enhance the process of strategic national planning significantly. Applying time series analysis to a comprehensive collection of historical population data, our methodology is able to provide valuable insights. Our team has utilized a range of machine learning models, such as Facebook's Prophet, LSTM (Long Short-Term Memory), State Space Model, Holt Winters, and SARIMA (Seasonal Autoregressive Integrated Moving Average). For processing time series data, each of these algorithms was carefully chosen based on its unique strengths. By utilizing a combination of these various models, we can guarantee a comprehensive and efficient population forecasting approach. The effectiveness of our methods is demonstrated by our encouraging findings. Highlighting its precision in forecasting, the Prophet algorithm achieved an impressively low Mean Absolute Percentage Error (MAPE) of just 0.48%. Reinforcing the accuracy of our approach, the LSTM model recorded a Root Mean Squared Error (RMSE) of 300020.64. Vast and impactful are the potential applications of this research. Crucial insights for various sectors, including urban development, resource management, and environmental planning, can be offered by providing more detailed and location-specific population predictions. Setting a foundation for future applications of machine learning in generating precise and dependable population predictions, this study makes a significant contribution to the field of demographic forecasting. Not just academic achievements, these advancements in forecasting methodologies serve as vital tools for policymakers and planners. They empower them to make more informed decisions for the betterment of national and regional development.