Computationally fast algorithms for ARMA spectral estimation

The high performance method for obtaining an ARMA model spectral estimate of a wide-sense stationary time series has been found to provide typically superior performance when compared to such contemporary approaches as the Box-Jenkins and maximum entropy methods. In this dissertation, fast recursive algorithmic implementations of the high performance method are developed. They are recursive in the sense that as a new element of the time series is observed, the parameters characterizing an ARMA spectral estimate are algorithmically updated. The number of multiplications and additions required at each recursive stage are of the order p with p being the number of denominator coefficients of the ARMA model. Methods of modification of the data are applied to achieve a significant computational improvement. The development is predicated on utilization of various projection operators.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Electrical Engineering

Department dc:contributor.department

Electrical Engineering

Grantor dc:publisher

Virginia Polytechnic Institute and State University

Year dc:date.issued

1981

Author dc:creator

• Ogino, Koji

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/10919/74833

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/74833