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Volumn 55, Issue 3 SUPPL. A, 1997, Pages 2557-2568

Forecasting chaotic time series with genetic algorithms

Author keywords

[No Author keywords available]

Indexed keywords

ASTROPHYSICS; CHAOS THEORY; DIFFERENTIAL EQUATIONS; FORECASTING; POPULATION STATISTICS; STATISTICAL MECHANICS; THEOREM PROVING; TIME SERIES ANALYSIS;

EID: 0031100934     PISSN: 1063651X     EISSN: None     Source Type: Journal    
DOI: 10.1103/physreve.55.2557     Document Type: Article
Times cited : (114)

References (45)
  • 6
    • 85086288518 scopus 로고    scopus 로고
    • note
    • t-λ the lag.
  • 32
    • 5544313274 scopus 로고    scopus 로고
    • In the language of search and optimization algorithms, mutation ensures that the population of agents does not get stuck on a local maximum in the "landscape" of the search-space
    • In the language of search and optimization algorithms, mutation ensures that the population of agents does not get stuck on a local maximum in the "landscape" of the search-space.
  • 33
    • 5544295944 scopus 로고    scopus 로고
    • In a manner of speech, "heavy" components of the equation may hide the "light" ones during the first run of the algorithm. Once the effect of the heavy components has been isolated, the genetic algorithm may - in the second stage - discover remaining parts of the formula
    • In a manner of speech, "heavy" components of the equation may hide the "light" ones during the first run of the algorithm. Once the effect of the heavy components has been isolated, the genetic algorithm may - in the second stage - discover remaining parts of the formula.
  • 35
    • 85086290420 scopus 로고    scopus 로고
    • t-z when reproducing equation strings
    • t-z when reproducing equation strings.
  • 37
    • 5544301208 scopus 로고    scopus 로고
    • note
    • When referring to one-period forecasts we simply mean the next entry in the time series, and similarly for n-period predictions. The period does not necessarily refer to the characteristic time of the chaotic process.
  • 40
    • 5544304275 scopus 로고    scopus 로고
    • note
    • The ability to predict such explosions would be a desirable feature of the algorithm. However, a training period could include at most one explosion which may not suffice to train the genetic algorithm for this type of occurrence. Further investigation is needed in this matter.
  • 42
    • 5544323508 scopus 로고    scopus 로고
    • note
    • The 11-year cycle that is generally believed to underlie the sunspot series, is recreated by Eq. (23), even when random initial data are used to produce different realizations of the series. The formulas bred by the algorithm could therefore provide an indication that the 11-cycle may actually be produced by a combination of 3- and 9-cycles.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.