Single-step and multiple-step forecasting in one-dimensional single chirp signal using MCMC-based Bayesian analysis
Satyaki Mazumder · Communications in Statistics - Simulation and Computation · 2015
Chirp signals are frequently used in different areas of science and engineering. MCMC-based Bayesian inference is done here for the purpose of one-step and multiple-step prediction in the case of the one-dimensional single chirp signal with iid error structure as well as dependent error structure with exponentially decaying covariances. We use Gibbs sampling technique and random walk MCMC to update the parameters. We perform total five simulation studies for the illustration purpose. We also do some real-data analysis to show how the method is working in practice.