Detection by a permutation test of periodicities in biological time series: Dependence of sensitivity on measurement precision

1976 ◽  
Vol 4 (1) ◽  
pp. 68-77 ◽  
Author(s):  
Stuart W. Smith ◽  
Robert H. Odell
Keyword(s):  
Time Series ◽  
Permutation Test ◽  
Measurement Precision ◽  
Biological Time ◽  
Series Dependence

scholarly journals Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods

2019 ◽  
Vol 3 (2) ◽  
pp. 274-306 ◽  
Author(s):  
Ruben Sanchez-Romero ◽  
Joseph D. Ramsey ◽  
Kun Zhang ◽  
Madelyn R. K. Glymour ◽  
Biwei Huang ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Methods ◽  
Permutation Test ◽  
Causal Structure ◽  
Multiple Model ◽  
New Methods ◽  
Test Models ◽  
Non Gaussian ◽  
True Structure ◽  
Better Than

We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear systems; Granger causal regression; a multivariate autoregressive model with a permutation test; the Group Iterative Multiple Model Estimation (GIMME) algorithm; the Ramsey et al. non-Gaussian methods; two non-Gaussian methods by Hyvärinen and Smith; a method due to Patel et al.; and the GlobalMIT algorithm. We introduce and also compare two new methods, Fast Adjacency Skewness (FASK) and Two-Step, both of which exploit non-Gaussian features of the BOLD signal. We give theoretical justifications for the latter two algorithms. Our test models include feedback structures with and without direct feedback (2-cycles), excitatory and inhibitory feedback, models using experimentally determined structural connectivities of macaques, and empirical human resting-state and task data. We find that averaged over all of our simulations, including those with 2-cycles, several of these methods have a better than 80% orientation precision (i.e., the probability of a directed edge is in the true structure given that a procedure estimates it to be so) and the two new methods also have better than 80% recall (probability of recovering an orientation in the true structure).

Time series dependence and spectral analysis

2022 ◽  
pp. 261-299
Author(s):  
Dag Tjøstheim ◽  
Håkon Otneim ◽  
Bård Støve
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Series Dependence

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.

Correcting for Cross-Sectional and Time-Series Dependence in Accounting Research

2010 ◽  
Vol 85 (2) ◽  
pp. 483-512 ◽  
Author(s):  
Ian D. Gow ◽  
Gaizka Ormazabal ◽  
Daniel J. Taylor
Keyword(s):  
Time Series ◽  
Equity Capital ◽  
Cost Of Debt ◽  
Test Statistics ◽  
Cost Of Equity Capital ◽  
Cost Of Equity ◽  
Cross Sectional ◽  
Accounting Research ◽  
The Cost ◽  
Series Dependence

ABSTRACT: We review and evaluate the methods commonly used in the accounting literature to correct for cross-sectional and time-series dependence. While much of the accounting literature studies settings in which variables are cross-sectionally and serially correlated, we find that the extant methods are not robust to both forms of dependence. Contrary to claims in the literature, we find that the Z2 statistic and Newey-West corrected Fama-MacBeth standard errors do not correct for both cross-sectional and time-series dependence. We show that extant methods produce misspecified test statistics in common accounting research settings, and that correcting for both forms of dependence substantially alters inferences reported in the literature. Specifically, several findings in the implied cost of equity capital literature, the cost of debt literature, and the conservatism literature appear not to be robust to the use of well-specified test statistics.

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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