Interpretable serious event forecasting using machine learning and SHAP

Sammanfattning: Accurate forecasts are vital in multiple areas of economic, scientific, commercial, and industrial activity. There are few previous studies on using forecasting methods for predicting serious events. This thesis set out to investigate two things, firstly whether machine learning models could be applied to the objective of forecasting serious events. Secondly, if the models could be made interpretable. Given these objectives, the approach was to formulate two forecasting tasks for the models and then use the Python framework SHAP to make them interpretable. The first task was to predict if a serious event will happen in the coming eight hours. The second task was to forecast how many serious events that will happen in the coming six hours. GBDT and LSTM models were implemented, evaluated, and compared on both tasks. Given the problem complexity of forecasting, the results match those of previous related research. On the classification task, the best performing model achieved an accuracy of 71.6%, and on the regression task, it missed by less than 1 on average.