A model of artificial biological time series generation

2021 ◽  
Author(s):  
Hyung-Rae Kim
Keyword(s):  
Time Series ◽  
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.

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.

Link Between Long-Term Variability in Upwelling and Fish Production in the Northeast Pacific Ocean

1991 ◽  
Vol 48 (12) ◽  
pp. 2296-2306 ◽  
Author(s):  
Daniel M. Ware ◽  
Richard E. Thomson
Keyword(s):  
Time Series ◽  
Primary Production ◽  
Southern California ◽  
Coastal Upwelling ◽  
Pelagic Fish ◽  
Biological Time ◽  
Long Period ◽  
Pacific Sardine ◽  
The Individual

The biomass of pelagic fish in the Coastal Upwelling Domain off the west coast of North America decreased by a factor of 5 in the first half of this century. We assemble several physical and biological time series spanning this period to determine what may have caused this decline in productivity. Based on an observed link between time series of the coastal wind and primary production, we conclude that there was a strong relaxation in wind-induced upwelling and primary production between 1916 and 1942 off southern California. The fact that the individual biomasses of the dominant pelagic fish species tend to rise and fall in phase through the sediment record off southern California is consistent with our belief that these species are responding to a long-period (40–60 yr) oscillation in primary and secondary production, which, in turn, is being forced by a long-period oscillation in wind-induced upwelling. Our extended sardine recruitment time series indicates that there is a nonlinear relationship between Pacific sardine (Sardinops sagax) recruitment and upwelling and suggests that optimal recruitment occurs when the wind speed during the first few months of life averages 7–8 m/s.

scholarly journals Visibility Graph Based Community Detection for Biological Time Series

2020 ◽  
Author(s):  
Minzhang Zheng ◽  
Sergii Domanskyi ◽  
Carlo Piermarocchi ◽  
George I. Mias
Keyword(s):  
Time Series ◽  
Community Detection ◽  
Shortest Path ◽  
Detection Algorithm ◽  
Visibility Graph ◽  
Main Stem ◽  
Biological Time ◽  
Link Type ◽  
Community Detection Algorithm ◽  
Biological Patterns

AbstractMotivationTemporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems: (i) How to create the appropriate network to reflect the characteristics of biological time series. (ii) How to detect characteristic temporal patterns or events as network communities. General methods to detect communities have used metrics to compare the connectivity within a community to the connectivity one would expect in a random model, or assumed a known number of communities, or are based on the betweenness centrality of edges or nodes. However, such methods were not specifically designed for network representations of time series. We introduce a visibility-graph-based method to build networks from different kinds of biological time series and detect temporal communities within these networks.ResultsTo characterize the uneven sampling of typical experimentally obtained biological time series, and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG) for time series. To detect communities, we first find the shortest path of the network between start and end nodes to identify nodes which have high intensities. This identifies the main stem of our community detection algorithm. Then, we aggregate nodes outside the shortest path to the nodes found on the main stem based on the closest path length. Through simulation, we demonstrate the validity of our method in detecting community structures on various networks derived from simulated time series. We also confirm its effectiveness in revealing temporal communities in experimental biological time series. Our results suggest our method of visibility graph based community detection can be effective in detecting temporal biological patterns.AvailabilityThe methods of building WDPVG and visibility graph based community detection are available as a module of the open source Python package PyIOmica (https://doi.org/10.5281/zenodo.3691912) with documentation at https://pyiomica.readthedocs.io/en/latest/. The dataset and codes we used in this manuscript are publicly available at https://doi.org/10.5281/[email protected]

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