One-step-ahead prediction of sunspots with genetic
programming
H. Jäske. Proceedings of the Second Nordic Workshop on Genetic
Algorithms and their Applications (2NWGA), Seite 79--88. Vaasa (Finland), University of Vaasa, (19.-23.~August 1996)
Zusammenfassung
Timeinvariant nonlinear one-step-ahead prediction
models were developed by genetic programming. As a test
case benchmark sunspot series was used. Functional form
and numerical parameters of the models were optimized.
The generalisation ability, i.e. final suitability, of
the predictors was assessed through crossvalidation.
The results were compared to those of threshold
autoregression and neural network -based predictors of
the sunspot benchmarks found in literature. Standard
GP-approach is shown not to be sufficient to solve this
prediction problem as well as the methods in comparison
do.
%0 Conference Paper
%1 ga96aJaske
%A Jäske, Harri
%B Proceedings of the Second Nordic Workshop on Genetic
Algorithms and their Applications (2NWGA)
%C Vaasa (Finland)
%D 1996
%E Alander, Jarmo T.
%I University of Vaasa
%K , algorithms, genetic prediction programming, series sunspots time
%P 79--88
%T One-step-ahead prediction of sunspots with genetic
programming
%U http://www.uwasa.fi/cs/publications/2NWGA/node70.html#SECTION04700000000000000000
%X Timeinvariant nonlinear one-step-ahead prediction
models were developed by genetic programming. As a test
case benchmark sunspot series was used. Functional form
and numerical parameters of the models were optimized.
The generalisation ability, i.e. final suitability, of
the predictors was assessed through crossvalidation.
The results were compared to those of threshold
autoregression and neural network -based predictors of
the sunspot benchmarks found in literature. Standard
GP-approach is shown not to be sufficient to solve this
prediction problem as well as the methods in comparison
do.
%Z *on,*FIN,genetic programming,astronomy /sunspots,time
series sunspots
@inproceedings{ga96aJaske,
abstract = {Timeinvariant nonlinear one-step-ahead prediction
models were developed by genetic programming. As a test
case benchmark sunspot series was used. Functional form
and numerical parameters of the models were optimized.
The generalisation ability, i.e. final suitability, of
the predictors was assessed through crossvalidation.
The results were compared to those of threshold
autoregression and neural network -based predictors of
the sunspot benchmarks found in literature. Standard
GP-approach is shown not to be sufficient to solve this
prediction problem as well as the methods in comparison
do.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Vaasa (Finland)},
annote = {*on,*FIN,genetic programming,astronomy /sunspots,time
series sunspots},
author = {J{\"a}ske, Harri},
biburl = {https://www.bibsonomy.org/bibtex/246fb2bd0aacc7380b121d22d8d104912/brazovayeye},
booktitle = {Proceedings of the Second Nordic Workshop on Genetic
Algorithms and their Applications (2NWGA)},
editor = {Alander, Jarmo T.},
interhash = {045ac97058c30bf87bca538452432344},
intrahash = {46fb2bd0aacc7380b121d22d8d104912},
keywords = {, algorithms, genetic prediction programming, series sunspots time},
month = {19.-23.~August},
notes = {lil-gp, non standard GP parameters? {"}evolved models
might not be numerically stable{"} page 86},
organisation = {Finnish Artificial Intelligence Society},
pages = {79--88},
publisher = {University of Vaasa},
series = {Proceedings of the University of Vaasa, Nro. 13},
timestamp = {2008-06-19T17:42:23.000+0200},
title = {One-step-ahead prediction of sunspots with genetic
programming},
url = {http://www.uwasa.fi/cs/publications/2NWGA/node70.html#SECTION04700000000000000000},
year = 1996
}