Publication:
Modelling and residual analysis of nonlinear auto-regressive time series in exponential variables

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Authors
Lewis, Peter A. W.
Lawrance, A. J.
Subjects
Advisors
Date of Issue
1984-08
Date
1984-08
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
An approach to modelling and residual analysis of nonlinear autoregressive time series in exponential variables is presented; the approach is illustrated by analysis of a long series of wind velocity data which has first been detrended and then transformed into a stationary series with an exponential marginal distribution. The stationary series is modelled with a newly developed type of second order autoregressive process with random coefficients, called the NEAR(2) model; it has a second order autoregressive correlation structure but is nonlinear because its coefficients are random. The exponential distributional assumptions involved in this model highlight a very broad four parameter structure which combines five exponential random variables into a sixth exponential random variable; other applications of this structure are briefly considered. Dependency in the NEAR(2) process not accounted for by standard autocorrelations is explored by developing a residual analysis for time series having autoregressive correlation structure; this involves defining linear uncorrelated residuals which are dependent, and then assessing this higher order dependence by standard time series computations. Application of this residual analysis to the wind velocity data illustrates both the utility and difficulty of nonlinear time series modelling
Type
Technical Report
Description
Series/Report No
Department
Identifiers
NPS Report Number
NPS55-84-019
Sponsors
Office of Naval Research
Funder
N0001484WR41001
Format
Citation
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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