Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models

These sources provide verified visual data on Russian equipment losses--including tanks, infantry fighting vehicles (IFVs), and support vehicles--enabling researchers to track material attrition at an unprecedented level of detail. Accurate forecasting of such losses is vital for military strategists, policymakers, and analysts attempting to model battlefield sustainability, logistics capacity, and broader trends in Russian force degradation. Traditional statistical models such as ARIMA offer a baseline for temporal forecasting, while more modern approaches--such as Prophet, LSTM (Long Short-Term Memory), Temporal Convolutional Networks (TCN), and XGBoost--introduce the ability to capture nonlinear dynamics, regime shifts, and short-term volatility. This paper evaluates each of these models using daily and monthly WarSpotting data. We assess their predictive accuracy, sensitivity to input granularity, and their robustness under shifting battlefield conditions.