scholarly journals Automatic network reconstruction from experimental time-series data: A survey

2014 ◽  
Vol 56 (2) ◽  
Author(s):  
Annegret Wagler
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Network Reconstruction ◽  
Series Data ◽  
Experimental Time ◽  
Experimental Time Series

scholarly journals Transient dynamic phenotypes as criteria for model discrimination: fold-change detection in Rhodobacter sphaeroides chemotaxis

2013 ◽  
Vol 10 (80) ◽  
pp. 20120935 ◽  
Author(s):  
Abdullah Hamadeh ◽  
Brian Ingalls ◽  
Eduardo Sontag
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Rhodobacter Sphaeroides ◽  
Time Series Data ◽  
Series Data ◽  
Transient Dynamic ◽  
Experimental Time ◽  
Theoretical Assumptions ◽  
Experimental Time Series ◽  
Fold Change Detection

The chemotaxis pathway of the bacterium Rhodobacter sphaeroides shares many similarities with that of Escherichia coli . It exhibits robust adaptation and has several homologues of the latter's chemotaxis proteins. Recent theoretical results have correctly predicted that the E. coli output behaviour is unchanged under scaling of its ligand input signal; this property is known as fold-change detection (FCD). In the light of recent experimental results suggesting that R. sphaeroides may also show FCD, we present theoretical assumptions on the R. sphaeroides chemosensory dynamics that can be shown to yield FCD behaviour. Furthermore, it is shown that these assumptions make FCD a property of this system that is robust to structural and parametric variations in the chemotaxis pathway, in agreement with experimental results. We construct and examine models of the full chemotaxis pathway that satisfy these assumptions and reproduce experimental time-series data from earlier studies. We then propose experiments in which models satisfying our theoretical assumptions predict robust FCD behaviour where earlier models do not. In this way, we illustrate how transient dynamic phenotypes such as FCD can be used for the purposes of discriminating between models that reproduce the same experimental time-series data.

Finding Unstable Fixed Points From Time Series Data

2003 ◽  
Author(s):  
Takaaki Maehara ◽  
Mikio Nakai
Keyword(s):  
Time Series ◽  
Fixed Points ◽  
Time Series Data ◽  
Piecewise Linear ◽  
Duffing Oscillator ◽  
Conley Index ◽  
Series Data ◽  
Experimental Time ◽  
Index Pairs ◽  
Experimental Time Series

This study employs topological methods to extract unstable fixed points in phase space from both numerical and experimental time series data. Conley index of an isolated invariant subset and the R-B method can determine unstable fixed points contained in strange attractor from numerical time series data. For experimental time series data, the theorem for the relationship between index pairs and Conley index enables one to predict them with acceptable accuracy. As a corollary, some results for Duffing oscillator and piecewise linear system are shown.

Measurement of Chaotic Behavior of Operator Stabilizing an Inverted Pendulum and Its Fuzzy Identification from Time Series Data

2001 ◽  
Vol 13 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Yoshihiko Kawazoe ◽  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Chaotic Behavior ◽  
Series Data ◽  
Experimental Time ◽  
Experimental Time Series ◽  
Simulated Time ◽  
Simulated Time Series

This paper investigates the identification of the chaotic characteristics of human operation with individual difference and the skill difference from the experimental time series data by utilizing fuzzy inference. It shows how to construct rules automatically for a fuzzy controller from experimental time series data of each trial of each operator to identify a controller from human-generated decision-making data. The characteristics of each operator trial were identified fairly well from experimental time series data by utilizing fuzzy reasoning. It was shown that the estimated maximum Lyapunov exponents of simulated time series data using an identified fuzzy controller were positive against embedding dimensions, which means a chaotic phenomenon. It was also recognized that the simulated human behavior have a large amount of disorder according to the result of estimated entropy from the simulated time, series data.

scholarly journals Temporal changes in species composition affect a ubiquitous species’ views of patch quality

2019 ◽  
Author(s):  
Ellen K. Bledsoe ◽  
S. K. Morgan Ernest
Keyword(s):  
Time Series ◽  
Species Composition ◽  
Energy Use ◽  
Time Series Data ◽  
Patch Dynamics ◽  
Temporal Changes ◽  
Series Data ◽  
Patch Quality ◽  
Experimental Time Series ◽  
Rodent Community

AbstractMetacommunity theory, particularly the patch dynamics archetype, suggests that an organism’s perspective of patch quality can depend solely on the local competitive environment. Across landscapes, however, shifts in species composition often co-occur with shifts in habitat, making it difficult to disentangle the role of competitors and environment on assessments of patch quality. Using 26 years of rodent community time-series data, we show that perception of patch quality by a small, ubiquitous granivore (Chaetodipus penicillatus) shifted with both spatial and temporal changes in species composition. In the mid-1990s,C. baileyi, a novel competitor, colonized and the study site.C. penicillatuspatch preference shifted with increasing abundance ofC. baileyi, including corresponding changes in estimated survival, probability of movement between patches, and the arrival of new individuals in patches. Changes in energy use on patches due to the establishment ofC. baileyipoint to a potential mechanism for the differences in patch quality perceived byC. penicillatus. These results demonstrate that experimental time-series data can be used to examine how changes in species composition and, specifically, changes in the competitive landscape, can affect perception of patch quality and patch preference.

scholarly journals A general deep learning framework for network reconstruction and dynamics learning

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Zhang Zhang ◽  
Yi Zhao ◽  
Jing Liu ◽  
Shuo Wang ◽  
Ruyi Tao ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Network Structure ◽  
Time Series Data ◽  
Network Reconstruction ◽  
Series Data ◽  
Learning Framework ◽  
Model Free ◽  
Reconstruction Methods ◽  
Free Data

AbstractMany complex processes can be viewed as dynamical systems on networks. However, in real cases, only the performances of the system are known, the network structure and the dynamical rules are not observed. Therefore, recovering latent network structure and dynamics from observed time series data are important tasks because it may help us to open the black box, and even to build up the model of a complex system automatically. Although this problem hosts a wealth of potential applications in biology, earth science, and epidemics etc., conventional methods have limitations. In this work, we introduce a new framework, Gumbel Graph Network (GGN), which is a model-free, data-driven deep learning framework to accomplish the reconstruction of both network connections and the dynamics on it. Our model consists of two jointly trained parts: a network generator that generating a discrete network with the Gumbel Softmax technique; and a dynamics learner that utilizing the generated network and one-step trajectory value to predict the states in future steps. We exhibit the universality of our framework on different kinds of time-series data: with the same structure, our model can be trained to accurately recover the network structure and predict future states on continuous, discrete, and binary dynamics, and outperforms competing network reconstruction methods.

IDENTIFYING PHASE SYNCHRONOUS REGIMES IN NON-COHERENT AND MULTIPLE SCROLL ATTRACTOR SYSTEMS

2013 ◽  
Vol 23 (11) ◽  
pp. 1350179 ◽  
Author(s):  
ROSANGELA FOLLMANN ◽  
EPAMINONDAS ROSA ◽  
ELBERT E. N. MACAU ◽  
JOSÉ ROBERTO CASTILHO PIQUEIRA
Keyword(s):  
Time Series ◽  
Phase Synchronization ◽  
Time Series Data ◽  
Series Data ◽  
Similarity Function ◽  
Attractor Reconstruction ◽  
Experimental Time Series ◽  
Data Output ◽  
Scalar Time Series

This work discusses the applicability of a method for phase determination of scalar time series from nonlinear systems. We apply the method to detect phase synchronization in different scenarios, and use the phase diffusion coefficient, the Lyapunov spectrum, and the similarity function to characterize synchronization transition in nonidentical coupled Rössler oscillators, both in coherent and non-coherent regimes. We also apply the method to detect phase synchronous regimes in systems with multiple scroll attractors as well as in experimental time series from coupled Chua circuits. The method is of easy implementation, requires no attractor reconstruction, and is particularly convenient in the case of experimental setups with a single time series data output.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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