Characterizing system dynamics with a weighted and directed network constructed from time series data

2014 ◽  
Vol 24 (2) ◽  
pp. 024402 ◽  
Author(s):  
Xiaoran Sun ◽  
Michael Small ◽  
Yi Zhao ◽  
Xiaoping Xue
Keyword(s):  
Time Series ◽  
System Dynamics ◽  
Time Series Data ◽  
Series Data ◽  
Directed Network

scholarly journals Windowed Granger causal inference strategy improves discovery of gene regulatory networks

2018 ◽  
Vol 115 (9) ◽  
pp. 2252-2257 ◽  
Author(s):  
Justin D. Finkle ◽  
Jia J. Wu ◽  
Neda Bagheri
Keyword(s):  
Time Series ◽  
Network Structure ◽  
Time Delays ◽  
Regulatory Networks ◽  
Network Inference ◽  
Time Series Data ◽  
Series Data ◽  
Directed Network ◽  
Rapid Characterization

Accurate inference of regulatory networks from experimental data facilitates the rapid characterization and understanding of biological systems. High-throughput technologies can provide a wealth of time-series data to better interrogate the complex regulatory dynamics inherent to organisms, but many network inference strategies do not effectively use temporal information. We address this limitation by introducing Sliding Window Inference for Network Generation (SWING), a generalized framework that incorporates multivariate Granger causality to infer network structure from time-series data. SWING moves beyond existing Granger methods by generating windowed models that simultaneously evaluate multiple upstream regulators at several potential time delays. We demonstrate that SWING elucidates network structure with greater accuracy in both in silico and experimentally validated in vitro systems. We estimate the apparent time delays present in each system and demonstrate that SWING infers time-delayed, gene–gene interactions that are distinct from baseline methods. By providing a temporal framework to infer the underlying directed network topology, SWING generates testable hypotheses for gene–gene influences.

Inferring groundwater system dynamics from hydrological time-series data

2010 ◽  
Vol 55 (4) ◽  
pp. 593-608 ◽  
Author(s):  
Angelo Doglioni ◽  
Davide Mancarella ◽  
Vincenzo Simeone ◽  
Orazio Giustolisi
Keyword(s):  
Time Series ◽  
System Dynamics ◽  
Time Series Data ◽  
Series Data ◽  
Groundwater System

Why Study Nonlinear Time Series Analysis?

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
System Dynamics ◽  
Real World ◽  
Time Series Data ◽  
Nonlinear Time Series ◽  
Series Data ◽  
Nonlinear Time Series Analysis ◽  
Deterministic System ◽  
Series Analysis

Nonlinear Time Series Analysis (NLTS) provides a mathematically rigorous collection of techniques designed to reconstruct real-world system dynamics from time series data on a single variable or multiple causally-related variables. NLTS facilitates scientific inquiry that emphasizes strong supportive evidence, well-conducted and thorough inquiry, and realism. Data provide an essential evidentiary portal to a reality to which we have only limited access. Random-appearing data do not prove that underlying dynamic process are subject to exogenous inherently-random forces. The possibility exists that observed volatility is generated by inherently-unstable, deterministic, and nonlinear real-world dynamic systems. NLTS allows the data to speak regarding which type of system dynamics generated them. It is capable of detecting linear as well as nonlinear deterministic system dynamics, and diagnosing the presence of linear stochastic dynamics. Our objective is to use NLTS to uncover the structure best corresponding to reality whether it be linear, nonlinear, deterministic, or stochastic. Accurate diagnosis of real-world dynamics from observed data is crucial to develop valid theory, and to formulate effective public policy based on theory.

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.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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