Explainable Forecasts of Disruptive Events using Recurrent Neural Networks

This paper describes the Crystal Cube method we developed for forecasting disruptive events around the world, specifically Irregular Leadership Change. Crystal Cube uses a Recurrent Neural Network (RNN) with Long-Short Term Memory (LSTM) units for forecasting. In this paper special emphasis is put on explanations of the network forecasts. We are using SHapley Additive exPlanations (SHAP) for individual forecast explanations and we are aggregating the explanations separately for True Positives, False Positives, True Negatives, and False Negatives. The method can be extended to Deep Reinforcement Learning models for self-driving cars or unmanned fighter jets.

Automated summary

This paper presents the Crystal Cube method for forecasting disruptive events worldwide, focusing on Irregular Leadership Change. The method utilizes a Recurrent Neural Network with Long-Short Term Memory units and emphasizes the explanation of network forecasts. SHapley Additive exPlanations is used for individual forecast explanations, and the method can be extended to Deep Reinforcement Learning models for self-driving cars or unmanned fighter jets.