Model-Based Clustering of Time Series Based on State Space Generative Models

摘要

Recently refined Markov Chain Monte Carlo techniques for Baye-sian inference are combined with the elegant and computationally advantageous specification of state space models to develop and evaluate an approach for the clustering of time series of fixed-income financial instruments. This approach is based upon the specification and estimation of a finite mixture model where each mixture component is represented by a time series generative model that is specified in linear state-space form.