scholarly journals Dynamic properties of the time series of biomedical measurement

2020 ◽  
Vol 46 (2) ◽  
Author(s):  
Павло Федорович Щапов ◽  
Ольга Борисівна Іванець ◽  
Оксана Сергіївна Севрюкова
Keyword(s):  
Time Series ◽  
Dynamic Properties ◽  
Biomedical Measurement

scholarly journals A geometric method of analysis for the light curves of active galactic nuclei

2020 ◽  
Author(s):  
Isao Shoji ◽  
Tadafumi Takata ◽  
Yoshihiko Mizumoto
Keyword(s):  
Time Series ◽  
Vector Field ◽  
Active Galactic Nuclei ◽  
Dynamic Properties ◽  
Flux Ratio ◽  
Galactic Nuclei ◽  
Light Curves ◽  
Geometric Method ◽  
Function Form ◽  
Filtering Method

Abstract We propose a geometric method of analysis for the light curves of active galactic nuclei (AGN). The time series of flux ratio is modeled by possibly nonlinear random oscillation without specifying the function form. Based on the model, we map the dynamic behavior of flux ratio to a vector field on a manifold, and then analyze the vector field to retrieve information on the dynamic properties closely linked with the activity of AGN. While the function form of the model is unspecified, the vector fields and those associated quantities can be estimated by applying a nonparametric filtering method. We illustrate the proposed analysis with an application to light curves of two AGNs supplied by the Kepler satellite. The application shows that the vector field, its derivative and their combination will be used as the tools of picking up various signals that help understanding of the activity of AGN. In addition, from a technical viewpoint, the nonparametric filtering method allows the estimation to be robust against outliers. The proposed analysis could be used as alternative time series analysis of the optical variability other than the analysis by spectral densities or structure functions.

Recurrence Quantification Techniques of vegetation time-series indices in semiarid grasslands

2020 ◽  
Author(s):  
Andres Almeida-Ñauñay ◽  
Rosa M. Benito ◽  
Miguel Quemada ◽  
Juan Carlos Losada ◽  
Ana Maria Tarquis
Keyword(s):  
Time Series ◽  
Recurrent Events ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Dynamic Properties ◽  
Distinct Pattern ◽  
Climatic Variables ◽  
Recurrence Quantification ◽  
Semi Arid

<p>Grassland ecosystems are extremely complex and set up intricate structures, whose characteristics and dynamic properties are greatly influenced by climate and meteorological patterns. Climate change and global warming are factors that could impact negatively in the quality and productivity of these ecosystems.</p><p>Remote sensing techniques have been demonstrated as a powerful tool for monitoring extensive areas. In this study, two semi-arid grassland plots were selected in the centre of Spain. This region is characterized by low precipitation and moderate productivity per unit. Through scientific research, spectral vegetation indices (VIs) have been developed to characterize vegetation cover. The most common VI is the Normalized Difference Vegetation Index (NDVI). However, in vegetation scarcity conditions, bare soil reflectance is increased, and the feasibility of NDVI is reduced. This study aims to perform a method to compare soil and agro-climatic variables effect on vegetation time-series indices.</p><p>The construction of the time series was based on multispectral images of MODIS TERRA (MOD09A1.006) product acquired from 2002 till 2018. Three pixels with a temporal resolution of 8 days and a spatial resolution of 500 x 500 m were chosen in each area. To estimate and analyse VIs series, Red (620-670 nm) and Near Infrared (841-876 nm) channels were extracted and filtered by the quality of pixel. All spectral bands showed statistically significant differences confirming that both areas presented different soil properties. Moreover, average annual precipitation was different in each area of study.</p><p>NDVI calculation is only based on NIR and RED bands. To improve the estimation of vegetation in semi-arid areas, several indices have been developed to minimize the soil effect. Each one of them incorporates soil influence in a different way, i.e., Soil Adjusted Vegetation Index (SAVI) adds a constant soil adjustment factor (L), whereas, MSAVI, incorporate an L variable and dependant on soil characteristics.</p><p>Recurrence plots (RP) and recurrence quantification analysis (RQA) were computed to characterize the influence of agro-climatic variables in vegetation index dynamics. Characterization was based on various RQA measures, such as Determinism (DET), average diagonal length (LT) or entropy (ENT).</p><p>Our results showed different RPs depending on the area, VI utilized and precipitation. MSAVI patterns were further distinct, meanwhile, NDVI showed a noisy pattern. LT values in MSAVI were higher than in SAVI implying that MSAVI recurrent events are much longer than SAVI. Simultaneously, LT and DET values in ZSO, with a higher rain, were above ZEA values in MSAVI.</p><p>This indicates that incorporating more detailed information of soil and precipitation reinforce vegetation index estimation and allow to obtain a more distinct pattern of the time series. Therefore, in arid-semiarid grasslands, they should be considered.</p><p><strong>ACKNOWLEDGEMENTS</strong></p><p>The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish <em>Ministerio de Ciencia Innovación y Universidades</em> of Spain and the funding from the Comunidad de Madrid (Spain) and Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330, are highly appreciated.</p>

CHRONOS: a time-varying method for microRNA-mediated subpathway enrichment analysis

2015 ◽  
Vol 32 (6) ◽  
pp. 884-892 ◽  
Author(s):  
Aristidis G. Vrahatis ◽  
Konstantina Dimitrakopoulou ◽  
Panos Balomenos ◽  
Athanasios K. Tsakalidis ◽  
Anastasios Bezerianos
Keyword(s):  
Time Series ◽  
Dynamic Properties ◽  
Enrichment Analysis ◽  
Microrna Expression ◽  
Supplementary Information ◽  
Pathway Enrichment Analysis ◽  
Analysis Tool ◽  
Time Varying ◽  
Microrna Target ◽  
Functional Features

Abstract Motivation: In the era of network medicine and the rapid growth of paired time series mRNA/microRNA expression experiments, there is an urgent need for pathway enrichment analysis methods able to capture the time- and condition-specific ‘active parts’ of the biological circuitry as well as the microRNA impact. Current methods ignore the multiple dynamical ‘themes’—in the form of enriched biologically relevant microRNA-mediated subpathways—that determine the functionality of signaling networks across time. Results: To address these challenges, we developed time-vaRying enriCHment integrOmics Subpathway aNalysis tOol (CHRONOS) by integrating time series mRNA/microRNA expression data with KEGG pathway maps and microRNA-target interactions. Specifically, microRNA-mediated subpathway topologies are extracted and evaluated based on the temporal transition and the fold change activity of the linked genes/microRNAs. Further, we provide measures that capture the structural and functional features of subpathways in relation to the complete organism pathway atlas. Our application to synthetic and real data shows that CHRONOS outperforms current subpathway-based methods into unraveling the inherent dynamic properties of pathways. Availability and implementation: CHRONOS is freely available at http://biosignal.med.upatras.gr/chronos/. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

SPECIAL PROPERTIES OF IMAGES AND CORRESPONDING SIGNALS GENERATED BY CHEMICAL REACTIONS DISCRETE CHAOTIC DYNAMICS

2007 ◽  
Vol 17 (10) ◽  
pp. 3415-3424 ◽  
Author(s):  
V. GONTAR ◽  
O. GRECHKO
Keyword(s):  
Time Series ◽  
Chemical Reactions ◽  
Discrete Time ◽  
Difference Equations ◽  
Chaotic Dynamics ◽  
Dynamic Properties ◽  
Bifurcation Diagrams ◽  
System Of Difference Equations ◽  
Different Types ◽  
Special Relations

A system of difference equations derived from chemical reactions discrete chaotic dynamics (DCD) is used to generate different types of images and corresponding discrete time series (signals). Dynamic properties of the generated time series and images are illustrated by partial bifurcation diagrams, partial intermediate bifurcation diagrams and Lyapunov exponents. Examples of images and signals have been presented. Some special relations between images and signals generated by DCD have been established and discussed.

scholarly journals Interpreting Null Models of Resting-State Functional MRI

2021 ◽  
Author(s):  
Raphaël Liégeois ◽  
B. T. Thomas Yeo ◽  
Dimitri Van De Ville
Keyword(s):  
Time Series ◽  
Dynamic Properties ◽  
Null Model ◽  
Model Testing ◽  
Null Models ◽  
Statistical Properties ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Fmri Time Series

AbstractNull models are necessary for assessing whether a dataset exhibits non-trivial statistical properties. These models have recently gained interest in the neuroimaging community as means to explore dynamic properties of functional Magnetic Resonance Imaging (fMRI) time series. Interpretation of null-model testing in this context may not be straightforward because (i) null hypotheses associated to different null models are sometimes unclear and (ii) fMRI metrics might be ‘trivial’, i.e. preserved under the null hypothesis, and still be useful in neuroimaging applications. In this commentary, we review several commonly used null models of fMRI time series and discuss the interpretation of the corresponding tests. We argue that, while null-model testing allows for a better characterization of the statistical properties of fMRI time series and associated metrics, it should not be considered as a mandatory validation step to assess their relevance in neuroimaging applications.

scholarly journals Chaotic Dynamics Analysis Based on Financial Time Series

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Zheng Gu ◽  
Yuhua Xu
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Dynamic Properties ◽  
Taylor Series Expansion ◽  
Scientific Methods ◽  
Financial Time ◽  
Bifurcation Phenomena ◽  
Expansion Form ◽  
Financial Research ◽  
The Empirical Analysis

It is a common phenomenon in the field of financial research to study the dynamic of financial market and explore the complexity of financial system by using various complex scientific methods. In this paper, the chaotic dynamic properties of financial time series are analyzed. Firstly, the nonlinear characteristics of the data are discussed through the empirical analysis of agriculture index data; the daily agriculture index returns can be decomposed into the different scales based on wavelet analysis. Secondly, the dynamic system of some nonlinear characteristic data is established according to the Taylor series expansion form, and the corresponding dynamic characteristics are analyzed. Finally, the bifurcation diagram of the system shows complicated bifurcation phenomena, which provides a perspective for the analysis of chaotic phenomena of economic data.

A data-driven approach to complex ecological systems

2020 ◽  
pp. 116-133
Author(s):  
Michio Kondoh ◽  
Kazutaka Kawatsu ◽  
Yutaka Osada ◽  
Masayuki Ushio
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Dynamic Properties ◽  
Spatial Scales ◽  
Interspecific Interaction ◽  
Community Level ◽  
Data Driven ◽  
Ecological Communities ◽  
The Real ◽  
Data Driven Approach

Interspecific interaction has been a key concept in ecology to understand the structure and dynamics of ecological communities. Important, yet often overlooked, is that an interspecific interaction is a product of multiple biological processes at various temporal and spatial scales, including changes in demographic parameters such as birth and death rates, behavioral responses such as inter-habitat movements, and hiding and evolutionary responses in a longer temporal scale. Each of those mechanisms, according to ecological theory, potentially affects population dynamics and modifies the community-level properties such as community complexity and stability in different manners. Here, a question arises: how does the net interspecific interaction, which is made up with those multiple processes, look like in the real nature? How do changes depend on the temporal or spatial scale? In this chapter we show that a data-driven approach using demographic time series is a powerful tool to answer those questions. According to nonlinear dynamics theory, a time series of a variable contains information about the dynamic system that the variable belongs to. We can use this fact to identify interspecific interactions, quantify their signs and strengths and evaluate its effect to community-level dynamic properties. Some results we got by applying the time-series analysis based on nonlinear dynamics theory (called Empirical Dynamic Modeling) to empirical demographic data, experimental or observational, will be presented, which will demonstrate how fluctuating and condition-dependent the real interactions are and reveal how those interactions give rise to the dynamic properties at higher organization levels.

scholarly journals ESTIMATING THE PERSISTENCE AND THE AUTOCORRELATION FUNCTION OF A TIME SERIES THAT IS MEASURED WITH ERROR

2013 ◽  
Vol 30 (1) ◽  
pp. 60-93 ◽  
Author(s):  
Peter R. Hansen ◽  
Asger Lunde
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Unit Root ◽  
Measurement Errors ◽  
Dynamic Properties ◽  
Unit Root Tests ◽  
Economic Time Series ◽  
Finite Sample ◽  
Latent Process ◽  
Near Unit Root

An economic time series can often be viewed as a noisy proxy for an underlying economic variable. Measurement errors will influence the dynamic properties of the observed process and may conceal the persistence of the underlying time series. In this paper we develop instrumental variable (IV) methods for extracting information about the latent process. Our framework can be used to estimate the autocorrelation function of the latent volatility process and a key persistence parameter. Our analysis is motivated by the recent literature on realized volatility measures that are imperfect estimates of actual volatility. In an empirical analysis using realized measures for the Dow Jones industrial average stocks, we find the underlying volatility to be near unit root in all cases. Although standard unit root tests are asymptotically justified, we find them to be misleading in our application despite the large sample. Unit root tests that are based on the IV estimator have better finite sample properties in this context.

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