Inverse Gaussian Autoregressive Models

Autoregressive Gaussian Random Vectors of First Order

Topology Selection in Graphical Models of Autoregressive Processes

An algorithm is presented for topology selection in graphical models of autoregressive Gaussian time series. The graph topology of the model represents the sparsity pattern of the inverse spectrum of the time series and characterizes conditional independence relations between the variables. The method proposed in the paper is based on an l1-type nonsmooth regularization of the conditional maxim...

Probabilistic forecasting of the disturbance storm time index: An autoregressive Gaussian process approach

We present a methodology for generating probabilistic predictions for the Disturbance Storm Time (Dst) geomagnetic activity index. We focus on the One Step Ahead prediction task and use the OMNI hourly resolution data to build our models. Our proposed methodology is based on the technique of Gaussian Process Regression. Within this framework we develop two models; Gaussian Process Autoregressiv...

Fractional normal inverse Gaussian diffusion

A fractional normal inverse Gaussian (FNIG) process is a fractional Brownian motion subordinated to an inverse Gaussian process. This paper shows how the FNIG process emerges naturally as the limit of a random walk with correlated jumps separated by i.i.d. waiting times. Similarly, we show that the NIG process, a Brownian motion subordinated to an inverse Gaussian process, is the limit of a ran...

Fast Computation of a Complex Quadratic Form

with t t t j ε ε ε Im Re + = , where } {Re t ε and } {Im t ε are independent real Gaussian white noise processes each with zero mean and variance 2 2 σ . These processes have been widely used to model the noise component in data models consisting of a deterministic signal and an additive noise. The pure harmonic signal, the damped harmonic signal, and the polynomial phase signal can be given as...