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Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models

Created by
  • Haebom

Author

Jonathan Teagan

Outline

This study applied various forecasting techniques, including ARIMA, Prophet, LSTM, TCN, and XGBoost, to predict Russian equipment losses during the Ukrainian War. Using daily and monthly open source intelligence (OSINT) data from WarSpotting, we aimed to assess loss reduction trends, validate model performance, and estimate future loss patterns through the end of 2025. Deep learning models, particularly TCN and LSTM, were found to produce stable and consistent predictions at high temporal granularity. A comparative analysis of different model architectures and input structures highlighted the importance of ensemble forecasting in conflict modeling and the value of publicly available OSINT data in quantifying material losses over time.

Takeaways, Limitations

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Takeaways:
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Deep learning models such as TCN and LSTM are shown to be effective in predicting Russian equipment losses in the Ukraine war.
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It suggests that open source intelligence (OSINT) data can be used to quantitatively analyze conflict situations in real time.
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We suggest that prediction accuracy can be improved through ensemble prediction techniques.
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We demonstrate that predictive performance improves when using data with high temporal granularity.
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Limitations:
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The need to verify the reliability and completeness of OSINT data.
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Further validation of the long-term predictive accuracy of the prediction model is needed.
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Limitations exist due to the uncertainty and unpredictability of war situations.
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There is a possibility of overfitting to a specific model or data.
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