Complex-time Representation, Statistical Inference, and AI Prediction Using Repeated Measurement Longitudinal Data

This tutorial from the APS GDS April Tutorial Series features Ivo Dinov from the University of Michigan discussing complex-time (kime) representation, statistical inference, and AI prediction techniques for repeated measurement longitudinal data. Learn about innovative approaches to analyzing time-series data using complex-time frameworks and how these methods can enhance statistical inference and artificial intelligence predictions when working with longitudinal datasets.