scholarly journals Wavelet Transforms in Sport: Application to Biological Time Series

10.5772/36292 ◽  
2012 ◽  
Author(s):  
Juan Manuel ◽  
Juan Manuel
Keyword(s):  
Time Series ◽  
Wavelet Transforms ◽  
Biological Time

Biological Time Series Analysis Using Complex Demodulation

2020 ◽  
pp. 429-457
Author(s):  
Daniel P. Redmond ◽  
Helen C. Sing ◽  
Frederick W. Hegge
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Biological Time ◽  
Complex Demodulation ◽  
Series Analysis

Nonlinear forecasting of non-uniform chaotic attractors in an enzyme reaction

1994 ◽  
Vol 348 (1688) ◽  
pp. 421-430
Keyword(s):  
Time Series ◽  
Enzyme Reaction ◽  
Experimental Conditions ◽  
Biological Time ◽  
Alternative Means ◽  
Nonlinear Forecasting ◽  
Linear Predictors ◽  
Short Term Forecasting ◽  
Low Dimensional ◽  
Frequent Sampling

Nonlinear forecasting was used to predict the time evolution of fluctuating concentrations of dissolved oxygen in the peroxidase-oxidase reaction. This reaction entails the oxidation of NADH with molecular oxygen as the electron acceptor. Depending upon the experimental conditions, either regular or highly irregular oscillations obtain. Previous work suggests that the latter fluctuations are almost certainly chaotic. In either case, the dynamics contain multiple timescales, which fact results in an uneven distribution of points in the phase space. Such ‘nonuniformity,’ as it is called, is a rock on which conventional methods for analysing chaotic time series often founder. The results of the present study are as follows. 1. Short-term forecasting with local linear predictors yields results that are consistent with a hypothesis of low-dimensional chaos. 2. Most of the evidence for nonlinear determinism disappears upon the addition of small amounts of observational error. 3. It is essentially impossible to make predictions over time intervals longer than the average period of oscillation for time series subject to continuous and frequent sampling. 4. Far more effective forecasting is possible for points on Poincare sections. 5. An alternative means for improving forecasting efficacy using the continuous data is to include a second variable (NADH concentration) in the analysis. Since non-uniformity is common in biological time series, we conclude that the application of nonlinear forecasting to univariate time series requires care both in implementation and interpretation.

Modulation of regional precipitation and groundwater level variability by large-scale oceanic/atmospheric circulation over interannual and interdecadal scales

2020 ◽  
Author(s):  
Nicolas Massei ◽  
Matthieu Fournier ◽  
Kwok Pan Chun ◽  
Qing He ◽  
Bastien Dieppois ◽  
...  
Keyword(s):  
Time Series ◽  
Atmospheric Circulation ◽  
Groundwater Level ◽  
Wavelet Transforms ◽  
Large Scale ◽  
Methodological Approach ◽  
Hydrological Processes ◽  
Seine River ◽  
Climate Signal ◽  
Interannual Scale

<p><span>Hydrological processes vary over long time-scales, which are originating from large-scale climate. It is not always straightforward, however, to identify how large-scale climate variability can affect regional or local-scale hydrological processes, as such relationships are not linear. Taking the example of the Seine river watershed (northern France), we study the modalities of precipitation and chalk aquifer groundwater level (GWL) variability, focusing on interannual (4-12 years) and interdecadal (12-23 years) scales. We propose a methodological approach for analysing and discussing potential large-scale relationships and forcings on hydrological systems. </span></p><p><span>163 GWL and 13 precipitation monthly time series, covering the northern half of metropolitan France between 1964 and 2015, were analysed using continuous and discrete multiresolution wavelet transforms. GWL time series all revealed statistically significant oscillating components on interannual and interdecadal scales, but with different amplitudes in space. All precipitation time series displayed the same oscillating components across the watershed with rather constant amplitudes spatially, contrary to GWL time series, which suggest an impact of local physical watershed properties to filter some parts of the climate signal. Using precipitation and GWL time series available over one century, as well as the NOAA 20CR reanalysis, we then analysed the relationship with the North Atlantic atmospheric circulation at both the interannual and interdecadal scales. On interannual scale, using sea-level pressure and geopotential height at 200 hPa, we found that precipitation and GWL variability would be linked to pronounced Rossby wave-like patterns. On interdecadal scale, the patterns obtained correspond to clear west-circulation patterns, which are very similar to the patterns associated with Atlantic Multidecadal Oscillation (AMO). Interdecadal precipitation variability are indeed also found to be consistent with the positive and negative phases of the AMO, suggesting potential impacts on hydrological variability. Examining both precipitation and GWL, major droughts occured during low levels of interannual and interdecadal components. This study therefore demonstrates that such extreme events would then be: i) linked to a weakened western circulation with strong North-South jet stream oscillations on interannual scale; ii) modulated by western circulation associated with the AMO on interdecadal scale.</span></p>

scholarly journals Cardiovascular and cardiorespiratory coupling analyses: a review

2013 ◽  
Vol 371 (1997) ◽  
pp. 20120191 ◽  
Author(s):  
Steffen Schulz ◽  
Felix-Constantin Adochiei ◽  
Ioana-Raluca Edu ◽  
Rico Schroeder ◽  
Hariton Costin ◽  
...  
Keyword(s):  
Time Series ◽  
Information Transfer ◽  
Phase Synchronization ◽  
Theoretical Background ◽  
Nonlinear Prediction ◽  
Prognostic Information ◽  
Driver Response ◽  
Biological Time ◽  
Cardiorespiratory Coupling ◽  
Medical Analysis

Recently, methods have been developed to analyse couplings in dynamic systems. In the field of medical analysis of complex cardiovascular and cardiorespiratory systems, there is growing interest in how insights may be gained into the interaction between regulatory mechanisms in healthy and diseased persons. The couplings within and between these systems can be linear or nonlinear. However, the complex mechanisms involved in cardiovascular and cardiorespiratory regulation very likely interact with each other in a nonlinear way. Recent advances in nonlinear dynamics and information theory have allowed the multivariate study of information transfer between time series. They therefore might be able to provide additional diagnostic and prognostic information in medicine and might, in particular, be able to complement traditional linear coupling analysis techniques. In this review, we describe the approaches (Granger causality, nonlinear prediction, entropy, symbolization, phase synchronization) most commonly applied to detect direct and indirect couplings between time series, especially focusing on nonlinear approaches. We will discuss their capacity to quantify direct and indirect couplings and the direction (driver–response relationship) of the considered interaction between different biological time series. We also give their basic theoretical background, their basic requirements for application, their main features and demonstrate their usefulness in different applications in the field of cardiovascular and cardiorespiratory coupling analyses.

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