Wavelet analysis of correlation among Canary Islands octopus captures per unit effort, sea-surface temperatures and the North Atlantic Oscillation

Polanco, Jorge; Ganzedo, Unai; Sáenz, Jon; Caballero-Alfonso, Ángela M.; Castro-Hernández, José Juan

doi:10.1016/j.fishres.2010.10.019

Cited by 16 publications

(7 citation statements)

References 68 publications

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“…The results were validated with an artificial TS and a real coastal sea level record. Moreover, Polanco et al [28] used the wavelet spectral analysis in order to analyze the SST and the NAO pattern. In fact, the XWT was applied to investigate the environmental effects (SST, NAO) on octopus abundance fluctuations measured as capture per unit of effort (CPUE) from 1989 to 2007 in the waters of the Canary Islands.…”

Section: Application Of Wt In Geo-sciences and Geophysicsmentioning

confidence: 99%

Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review

et al. 2019

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Non-stationary time series (TS) analysis has gained an explosive interest over the recent decades in different applied sciences. In fact, several decomposition methods were developed in order to extract various components (e.g., seasonal, trend and abrupt components) from the non-stationary TS, which allows for an improved interpretation of the temporal variability. The wavelet transform (WT) has been successfully applied over an extraordinary range of fields in order to decompose the non-stationary TS into time-frequency domain. For this reason, the WT method is briefly introduced and reviewed in this paper. In addition, this latter includes different research and applications of the WT to non-stationary TS in seven different applied sciences fields, namely the geo-sciences and geophysics, remote sensing in vegetation analysis, engineering, hydrology, finance, medicine, and other fields, such as ecology, renewable energy, chemistry and history. Finally, five challenges and future works, such as the selection of the type of wavelet, selection of the adequate mother wavelet, selection of the scale, the combination between wavelet transform and machine learning algorithm and the interpretation of the obtained components, are also discussed.

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Section: Application Of Wt In Geo-sciences and Geophysicsmentioning

confidence: 99%

Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review

et al. 2019

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“…For example, the wavelet correlation (obtained either by means of cross-correlation or by wavelet coherence) estimated through the CWT easily allows the evolution of correlation and the co-movements between two time series in time and scale (frequency) to be analysed at once [2,48]. Furthermore, the CWT can be used with complex wavelet functions, such as Morlet; therefore, it is possible to estimate the phase difference (or phase coherence), which is very useful to obtain information on the delay or synchronisation (lead-lag relationships) between oscillations of the two time series under study [2,61].…”

Section: Rolling Window Spearman Correlationmentioning

confidence: 99%

“…The wavelet Morlet provides a good balance between scale (frequencies) and time localisations [34,51] and is one of the best mother functions in terms of reproducing frequency [37,61]. However, despite the WCO method (via Morlet) of Maraun and Kurths ([48]) having been successfully used to analyse different kinds of time series (e.g.…”

Section: Rolling Window Spearman Correlationmentioning

confidence: 99%

Dynamic relationship analysis between NAFTA stock markets using nonlinear, nonparametric, non-stationary methods

Polanco-Martínez

2019

Nonlinear Dyn

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This paper seeks to investigate the dynamic relationship between daily stock market indices in NAFTA countries from 8 November 1991 to 16 March 2018, using for the first time nonlinear, nonparametric, non-stationary methods. We apply two novel nonlinear, nonparametric, non-stationary dynamic correlation techniques-rolling window Spearman correlation and wavelet coherence-to study the relationships between the three pairwise comparisons. We apply a nonlinear, nonparametric causality test to four specific subperiods and to the full period of these indices to check the direction of causality. Our results show the following: (1) the correlation between the indices increases from 2000 to 2011, but that correlation increase is interrupted around 2011/2012 and then falls noticeably, picking up again from 2015 onwards. (2) The pairs that show the lowest correlation are those involving the IPC.(3) The causality test reveals nonlinear bidirectional causality for all three indices and all the intervals analysed, indicating that there is a strong interrelationship between NAFTA members. These results are relevant to obtain a better understanding of the complex dynamical system formed by NAFTA stock markets and have

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“…Faria, 2018. Estimation of the significance of the Foster's... Boletín Geológico y Minero,129 (3): 549-564 tion a and time localizations (Grinsted et al, 2004;Mi et al, 2005;Polanco et al, 2011), so that it is considered one of the best mother functions in terms of reproducing the frequency decomposition of a time series (Kirby, 2005;Li et al, 2007).…”

Section: A) the Continuous Wavelet Spectral Analysismentioning

confidence: 99%

Estimation of the significance of the Foster’s wavelet spectrum by means of a permutation test and its application for paleoclimate records

Polanco-Martínez¹,

Faria²

2018

Bol. geol. min.

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Here we propose a permutation test -a non-parametric computing-intensive test-to evaluate the statistical significance of the Foster's wavelet spectrum by means of Monte Carlo simulations. A procedure (algorithm) is introduced in order to carry out this aim. The Foster's wavelet is an adequate method to cope directly with unevenly spaced paleoclimatic time series. We have conducted time series simulations to study the performance of the Foster's wavelet spectrum and applied the permutation test to localize periodic signals known a priori by randomly increasing the fraction of missing data (from 25% to 75% of the total amount of data of an evenly spaced time series). We found that the periodic signals are progressively lost as larger amounts of data are removed. This loss becomes noticeable at circa 50% of the missing data and gets more evident when 75% of the data is removed. The signal loss is more pronounced in the high-frequency range, due to an intrinsic bias in the wavelet spectrum. Notwithstanding, the Foster's wavelet spectrum and the permutation test succeeded in locating the periodic signals, even with a moderate amount of missing data. Finally, we applied our procedure to the oxygen isotope ratio (δ 18 O) data of the GISP2 deep ice core (Greenland) and we were able to detect the main spectral signature of the unevenly spaced time series of the GISP2 δ 18 O record, i.e., the spectral peak around 1,470 years.

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Wavelet analysis of correlation among Canary Islands octopus captures per unit effort, sea-surface temperatures and the North Atlantic Oscillation

Cited by 16 publications

References 68 publications

Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review

Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review

Dynamic relationship analysis between NAFTA stock markets using nonlinear, nonparametric, non-stationary methods

Estimation of the significance of the Foster’s wavelet spectrum by means of a permutation test and its application for paleoclimate records

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