An in-Depth Analysis of Military Casualties: Predicting Russian Losses in the Russia-Ukraine Conflict

Abstract

This research on the Russia-Ukraine conflict employs sophisticated data science methods and time series forecasting techniques to analyze Russian military casualties within a specific timeframe. The study aims to unravel the intricate dynamics of conflict by scrutinizing complex patterns and trends in the available data. The research encompasses a thorough examination of casualties, including soldiers, equipment, and vehicles, with the incorporation of key performance metrics like accuracy, MAE, MSE, RMSE, and R2. These metrics provide a quantitative assessment of forecasting models, enhancing the analysis by offering insights into the reliability and predictive capabilities of these models. The inclusion of forecasting models introduces a prognostic element, contributing valuable perspectives on potential future scenarios. The results not only enhance understanding of the ongoing conflict but also offer insights crucial for military decision-makers, politicians, and scholars involved in strategic analysis and risk assessment. By integrating advanced analytical techniques and performance metrics, this research aspires to provide a comprehensive and well-informed perspective on the evolving dynamics of the conflict, facilitating more effective decision-making in the realms of military strategy and policy.