This paper deals with clustering of segments of stock
prices by using nonlinear modelling system for time
series based on the Genetic Programming (GP). We apply
the GP procedure in learning phase of the system where
we improve the nonlinear functional forms to
approximate the models used to generate time series.
The variation of the individuals with relatively high
capability in the pool can cope with clustering for
various kinds of time series which belong to the same
cluster similar to the classifier systems. As an
application, we show clustering of artificially
generated time series obtained by expanding or
shrinking by transformation functions. Then, we apply
the system to clustering of 8 kinds of segments of real
stock prices.
%0 Conference Paper
%1 Lu:2006:MLC
%A Lu, Jian-Jun
%A Liu, Yun-Ling
%A Tokinaga, Shozo
%B International Conference on Machine Learning and
Cybernetics
%C Dalian
%D 2006
%I IEEE
%K algorithms, genetic programming
%P 2097--2102
%R doi:10.1109/ICMLC.2006.258350
%T Nonlinear Modeling for Time Series Based on the
Genetic Programming and its Applications
%X This paper deals with clustering of segments of stock
prices by using nonlinear modelling system for time
series based on the Genetic Programming (GP). We apply
the GP procedure in learning phase of the system where
we improve the nonlinear functional forms to
approximate the models used to generate time series.
The variation of the individuals with relatively high
capability in the pool can cope with clustering for
various kinds of time series which belong to the same
cluster similar to the classifier systems. As an
application, we show clustering of artificially
generated time series obtained by expanding or
shrinking by transformation functions. Then, we apply
the system to clustering of 8 kinds of segments of real
stock prices.
%@ 1-4244-0061-9
@inproceedings{Lu:2006:MLC,
abstract = {This paper deals with clustering of segments of stock
prices by using nonlinear modelling system for time
series based on the Genetic Programming (GP). We apply
the GP procedure in learning phase of the system where
we improve the nonlinear functional forms to
approximate the models used to generate time series.
The variation of the individuals with relatively high
capability in the pool can cope with clustering for
various kinds of time series which belong to the same
cluster similar to the classifier systems. As an
application, we show clustering of artificially
generated time series obtained by expanding or
shrinking by transformation functions. Then, we apply
the system to clustering of 8 kinds of segments of real
stock prices.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Dalian},
author = {Lu, Jian-Jun and Liu, Yun-Ling and Tokinaga, Shozo},
biburl = {https://www.bibsonomy.org/bibtex/249c9626964e601a9f8eb8e15a15190cf/brazovayeye},
booktitle = {International Conference on Machine Learning and
Cybernetics},
doi = {doi:10.1109/ICMLC.2006.258350},
interhash = {43632d9ab2a2c8dd6223b1ee5dd7da1f},
intrahash = {49c9626964e601a9f8eb8e15a15190cf},
isbn = {1-4244-0061-9},
keywords = {algorithms, genetic programming},
month = {August},
notes = {Graduate School of Economics, Kyushu University,
Fukuoka 812-8581, Japan},
pages = {2097--2102},
publisher = {IEEE},
timestamp = {2008-06-19T17:45:54.000+0200},
title = {Nonlinear Modeling for Time Series Based on the
Genetic Programming and its Applications},
year = 2006
}