用深度学习预测俄乌战争中俄军装备损失趋势，精度高于传统模型。
Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models
- 结合时间序列与深度学习模型，利用开源情报数据建模。
- TCN和LSTM在日度数据下表现最佳，预测稳定可靠。
- 适合关注冲突量化分析与军事损耗研究的读者。
本研究应用ARIMA、Prophet、LSTM、TCN和XGBoost等多种预测方法，基于WarSpotting提供的每日及每月开源情报（OSINT）数据，对乌克兰战争中俄罗斯装备损失进行建模与预测，旨在评估损耗趋势、比较模型性能，并预测至2025年底的损失模式。结果表明，深度学习模型（尤其是TCN和LSTM）在高时间粒度条件下表现优异，具有稳定且一致的预测能力。通过对比不同模型架构与输入结构，研究强调了集成预测在冲突建模中的重要性，以及公开可用的OSINT数据在量化物质损耗方面的价值。
原文摘要 · Abstract (English)
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.
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