Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction.
We introducemixed LICORS, an algorithm for learning nonlinear, high-dimensional dynamics from spatio-temporal data, suitable for both prediction and simulation. Mixed LICORS extends the recent LICORS algorithm (Goerg and Shalizi, 2012) from hard clustering of predictive distributions to a non-parametric, EM-like soft clustering. This retains the asymptotic predictive optimality of LICORS, but, as we show in simulations, greatly improves out-of-sample forecasts with limited data. The new method is implemented in the publicly-available R package LICORS.
Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213.
Georg M
GM
Goerg
Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213.
Cosma Rohilla
CR
Shalizi