Single step and multiple step forecasting in one dimensional single chirp signal using MCMC based Bayesian analysis

TL;DR

This paper develops a Bayesian MCMC approach for one-step and multi-step forecasting of one-dimensional single chirp signals, incorporating different error structures, and demonstrates its effectiveness through simulations and real data analysis.

Contribution

It introduces a Bayesian MCMC method for chirp signal forecasting that handles both independent and dependent error structures, with comprehensive simulation and real data validation.

Findings

Effective forecasting demonstrated through simulations.

Method handles dependent error structures with exponential decay.

Real data analysis confirms practical applicability.

Abstract

Chirp signals are frequently used in different areas of science and engineering. MCMC based Bayesian inference is done here for purpose of one step and multiple step prediction in case of one dimensional single chirp signal with i.\ i.\ d.\ 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 illustration purpose. We also do some real data analysis to show how the method is working in practice.