Identifying a non-normal evolving stochastic process based upon the genetic methods

Kangrong Tan, Meifen Chu, Shozo Tokinaga

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

In the real world, many evolving stochastic processes appear heavy tails, excess kurtosis, and other non-normal evidences, though, they eventually converge to normals due to the central limit theorem, and the augment effect. So far many studies focusing on the normal cases, such as Brownian Motion, or Geometric Brownian Motion etc, have shown their restrictions in dealing with non-normal phenomena, although they have achieved a great deal of success. Moreover, in many studies, the statistical properties, such as the distributional parameters of an evolving process, have been studied at a special time spot, not having grasped the whole picture during the whole evolving time period. In this paper, we propose to approximate an evolving stochastic process based upon a process characterized by a time-varying mixture distribution family to grasp the whole evolving picture of its evolution behavior. Good statistical properties of such a time-varying process are well illustrated and discussed. The parameters in such a time-varying mixture distribution family are optimized by the Genetic Methods, namely, the Genetic Algorithm (GA) and Genetic Programming (GP). Numerical experiments are carried out and the results prove that our proposed approach works well in dealing with a non-normal evolving stochastic process.

本文言語英語
ホスト出版物のタイトルIntegrated Uncertainty in Knowledge Modelling and Decision Making - International Symposium, IUKM 2011, Proceedings
ページ168-178
ページ数11
DOI
出版ステータス出版済み - 10 28 2011
イベント2011 International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2011 - Hangzhou, 中国
継続期間: 10 28 201110 30 2011

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
7027 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他2011 International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2011
国/地域中国
CityHangzhou
Period10/28/1110/30/11

All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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