Abstract
This study applies a range of forecasting techniques,including ARIMA, Prophet, Long Short Term Memory networks (LSTM), Temporal Convolutional Networks (TCN), and XGBoost, to model and predict Russian equipment losses during the ongoing war in Ukraine. Drawing on daily and monthly open-source intelligence (OSINT) data from WarSpotting, we aim to assess trends in attrition, evaluate model performance, and estimate future loss patterns through the end of 2025. Our findings show that deep learning models, particularly TCN and LSTM, produce stable and consistent forecasts, especially under conditions of high temporal granularity. By comparing different model architectures and input structures, this study highlights the importance of ensemble forecasting in conflict modeling, and the value of publicly available OSINT data in quantifying material degradation over time.
Abstract (translated)
这项研究应用了一系列预测技术，包括ARIMA、Prophet、长短期记忆网络（LSTM）、时间卷积网络（TCN）和XGBoost，来建模并预测俄罗斯在乌克兰战争中装备的损失。我们利用WarSpotting提供的每日和月度开源情报（OSINT）数据，旨在评估损耗趋势、评估模型性能，并估计到2025年底的未来损失模式。我们的研究发现表明，深度学习模型，尤其是TCN和LSTM，在时间粒度高的条件下能产生稳定且一致的预测结果。通过比较不同模型架构和输入结构，本研究强调了冲突建模中的集成预测的重要性以及公开可用OSINT数据在量化材料损耗方面的时间价值。
URL
https://arxiv.org/abs/2509.07813
