Prediction and Non-Gaussian Autoregressive Stationary Sequences

Prediction and Nongaussian Autoregressive Stationary Sequences

The object of this paper is to show that under certain auxiliary assumptions a stationary autoregressive sequence has a best predictor in mean square that is linear if and only if the sequence is minimum phase or is Gaussian when all moments are finite.

Non-stationary autoregressive filters for prediction of subsurface geological structure

Accurate decision-making in the petroleum industry is highly contingent on building a reliable model of the subsurface geological structure. Building a model of the subsurface typically involves solving an inverse problem with acquired data for various model parameters of interest like P-wave velocity, rock porosity etc. However, issues of poor data quality necessitate regularizing the inverse ...

Stationary space-time Gaussian fields and their time autoregressive representation

We compare two different modelling strategies for continuous space discrete time data. The Žrst strategy is in the spirit of Gaussian kriging. The model is a general stationary space–time Gaussian Želd where the key point is the choice of a parametric form for the covariance function. In the main, covariance functions that are used are separable in space and time. Nonseparable covariance functi...

Fast Algorithm for Non-Stationary Gaussian Process Prediction

The FNSGP algorithm for Gaussian process model is proposed in this paper. It reduces the time cost to accelerate the task of non-stationary time series prediction without loss of accuracy. Some experiments are verified on the real world power load data.

Autoregressive Gaussian Random Vectors of First Order