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.