Header

UZH-Logo

Maintenance Infos

Quantitative Assessment of the Log-Log-Step Method for Pattern Detection in Noise-Prone Environments


Gomez, F; Stoop, R (2011). Quantitative Assessment of the Log-Log-Step Method for Pattern Detection in Noise-Prone Environments. PLoS ONE, 6(12):e28107.

Abstract

Staircase-like structures in the log-log correlation plot of a time series indicate patterns against a noisy background, even under condition of strong jitter. We analyze the method for different jitter-noise-combinations, using quantitative criteria to measure the achievement by the method. A phase diagram shows the remarkable potential of this method even under very unfavorable conditions of noise and jitter. Moreover, we provide a novel and compact analytical derivation of the upper and lower bounds on the number of steps observable in the ideal noiseless case, as a function of pattern length and embedding dimension. The quantitative measure developed combined with the ideal bounds can serve as guiding lines for determining potential periodicity in noisy data.

Abstract

Staircase-like structures in the log-log correlation plot of a time series indicate patterns against a noisy background, even under condition of strong jitter. We analyze the method for different jitter-noise-combinations, using quantitative criteria to measure the achievement by the method. A phase diagram shows the remarkable potential of this method even under very unfavorable conditions of noise and jitter. Moreover, we provide a novel and compact analytical derivation of the upper and lower bounds on the number of steps observable in the ideal noiseless case, as a function of pattern length and embedding dimension. The quantitative measure developed combined with the ideal bounds can serve as guiding lines for determining potential periodicity in noisy data.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Downloads

49 downloads since deposited on 03 Sep 2014
2 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Life Sciences > General Biochemistry, Genetics and Molecular Biology
Life Sciences > General Agricultural and Biological Sciences
Health Sciences > Multidisciplinary
Language:English
Date:2011
Deposited On:03 Sep 2014 13:05
Last Modified:11 Jun 2024 01:57
Publisher:Public Library of Science (PLoS)
Series Name:PloS One
Number of Pages:1
ISSN:1932-6203
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1371/journal.pone.0028107
PubMed ID:22174769
  • Content: Published Version
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)