Many forecasts consist not of point predictions but concern the evolution of quantities. For example, a central bank might predict the interest rates during the next quarter, an epidemiologist might predict trajectories of infection rates, a clinician might predict the behaviour of medical markers over the next day, etc. The situation is further complicated since these forecasts sometimes only concern the approximate "shape of the future evolution" or "order of events". Formally, such forecasts can be seen as probability measures on spaces of equivalence classes of paths modulo time-parametrization. We leverage the statistical framework of proper scoring rules with classical mathematical results to derive a principled approach to decision making with such forecasts.
Patric is a PhD student under the supervision of Harald Oberhauser. His interests are signature methods in machine learning and statistics. Before coming to Oxford for his PhD, he was awarded a BSc in physics from KTH Stockholm and did Part III at Cambridge.
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