Unsupervised Feature Extraction Clustering for Crisis Prediction

This paper focuses on macroeconomic forecasting literature.We introduce unFEAR, an unsupervised feature extraction clustering method aimed at facilitating crisis prediction tasks. We use unsupervised representation learning and a novel autoencoder method to extract from economic data information relevant to identify time-invariant non-overlapping clusters comprising observed crisis and non-crisis episodes. Each cluster corresponds to a different economic regime characterized by an idiosyncratic crisis generating mechanism.