Topological data analysis can help predict stock-market crashes

By closely monitoring a system's data point clouds, scientists can identify the system's normal state. DR - Scientists, together with local startup L2F, have developed a robust model that can predict when a systemic shift is about to occur, based on methods from a branch of mathematics called topological data analysis. Topological data analysis (TDA) involves extracting information from clouds of data points and using the information to classify data, recognize patterns or predict trends, for example. A team of scientists from EPFL's Laboratory for Topology and Neuroscience, L2F (an EPFL spin-off), and HEIG-VD, working on a project funded in part by an Innosuisse grant, used TDA to develop a model that can predict when a system is about to undergo a major shift. Their model, called giotto-tda , is available as an open-source library and can help analysts identify when events like a stock-market crash, earthquake, traffic jam, coup d'etat or train-engine malfunction are about to occur. Catastrophes and other unexpected events are by definition aberrations - that's what makes them hard to predict with conventional models. The research team therefore drew on methods from TDA to come up with a novel approach based on the fact that when a system reaches a critical state, such as when water is about to solidify into ice, the data points representing the system begin to form shapes that change its overall structure.