Title :
A first-order AR model for non-Gaussian time series
Author :
Rao, P. Srinivasa ; Johnson, Don H.
Author_Institution :
Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX, USA
Abstract :
A simple first-order autoregressive model for the generation of non-Gaussian time series is described. It is defined by Xn =ρXn-1+Wn and has a hyperbolic secant marginal distribution. This hyperbolic secant model can be used to generate random non-Gaussian sequences which are free of the degeneracy that afflicts the sequences generated using the Laplace model. The generation formula and the bivariate distributions of this model are derived. It is shown that the mean-square (MS) backward prediction error is strictly less than the MS forward prediction error for all first-order autoregressive non-Gaussian models
Keywords :
signal processing; time series; backward prediction error; bivariate distributions; first-order autoregressive model; forward prediction error; hyperbolic secant model; mean square error; nonGaussian time series; Density functional theory; Encoding; Hydrogen; Laplace equations; Noise generators; Predictive models; Random variables; Tail; Time series analysis; White noise;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on
Conference_Location :
New York, NY
DOI :
10.1109/ICASSP.1988.196896
