Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloMost probable dimension value and most flat interval methods for automatic estimation of dimension from time series
Anno di pubblicazione2004
FormatoElettronico
Autore/iAngelo Corana; Giovanni Bortolan; Aldo Casaleggio
Affiliazioni autoriIEIIT-CNR, Via De Marini 6, 16149 Genova, Italy ISIB-CNR, Corso Stati Uniti 4, 35127 Padova, Italy IBF-CNR, Via De Marini 6, 16149 Genova, Italy
Autori CNR e affiliazioni
  • GIOVANNI BORTOLAN VQR
  • ALDO CASALEGGIO VQR
  • ANGELO CORANA VQR
Lingua/e
  • inglese
AbstractWe present and compare two automatic methods for dimension estimation from time series. Both methods, based on conceptually different approaches, work on the derivative of the bi-logarithmic plot of the correlation integral versus the correlation length (log–log plot). The first method searches for the most probable dimension values (MPDV) and associates to each of them a possible scaling region. The second one searches for the most flat intervals (MFI) in the derivative of the log–log plot. The automatic procedures include the evaluation of the candidate scaling regions using two reliability indices. The data set used to test the methods consists of time series from known model attractors with and without the addition of noise, structured time series, and electrocardiographic signals from the MIT-BIH ECG database. Statistical analysis of results was carried out by means of paired t-test, and no statistically significant differences were found in the large majority of the trials. Consistent results are also obtained dealing with ‘difficult’ time series. In general for a more robust and reliable estimate, the use of both methods may represent a good solution when time series from complex systems are analyzed. Although we present results for the correlation dimension only, the procedures can also be used for the automatic estimation of generalized q-order dimensions and pointwise dimension. We think that the proposed methods, eliminating the need of operator intervention, allow a faster and more objective analysis, thus improving the usefulness of dimension analysis for the characterization of time series obtained from complex dynamical systems.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da779
Pagine a790
Pagine totali-
RivistaChaos, solitons and fractals
Attiva dal 1991
Editore: Pergamon. - Oxford
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 0960-0779
Titolo chiave: Chaos, solitons and fractals
Titolo abbreviato: Chaos, solitons fractals
Numero volume della rivista20
Fascicolo della rivista4
DOI10.1016/j.chaos.2003.08.012
Verificato da refereeSì: Internazionale
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000187858200014)
  • Scopus (Codice:2-s2.0-0345356240)
  • MathSciNet (Mathematical Reviews on the web) (Codice:MR2027307)
Parole chiavetime series analysis, dimension, automatic estimation
Link (URL, URI)http://www.sciencedirect.com/science/article/pii/S0960077903004545
Titolo parallelo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IBF — Istituto di biofisica
  • IEIIT — IEIIT - Sede secondaria di Genova
  • ISIB — Istituto di ingegneria biomedica
Moduli CNR
    Progetti Europei-
    Allegati
    • Casaleggio_2004_CSF

    Dati storici
    I dati storici non sono modificabili, sono stati ereditati da altri sistemi (es. Gestione Istituti, PUMA, ...) e hanno solo valore storico.
    Area disciplinareComputer Science & Engineering
    Area valutazione CIVRScienze matematiche e informatiche
    Rivista ISICHAOS SOLITONS & FRACTALS [10250J0]