Automatic reconstruction of molecular and genetic networks from discrete time series data

We present a new statistical approach to analyse epidemic time-series data. A major difficulty for inference is that (i) the latent transmission process is partially observed and (ii) observed quantities are further aggregated temporally. We develop a data augmentation strategy to tackle these problems and introduce a diffusion process that mimicks the susceptible–infectious–removed (SIR) epidemic process, but that is more tractable analytically. While methods based on discrete-time models require epidemic and data collection processes to have similar time scales, our approach, based on a continuous-time model, is free of such constraint. Using simulated data, we found that all parameters of the SIR model, including the generation time, were estimated accurately if the observation interval was less than 2.5 times the generation time of the disease. Previous discrete-time TSIR models have been unable to estimate generation times, given that they assume the generation time is equal to the observation interval. However, we were unable to estimate the generation time of measles accurately from historical data. This indicates that simple models assuming homogenous mixing (even with age structure) of the type which are standard in mathematical epidemiology miss key features of epidemics in large populations.

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Reconstructing bifurcation diagrams only from time-series data generated by electronic circuits in discrete-time dynamical systems

Chaos An Interdisciplinary Journal of Nonlinear Science ◽

10.1063/1.5119187 ◽

2020 ◽

Vol 30 (1) ◽

pp. 013128

Author(s):

Y. Itoh ◽

S. Uenohara ◽

M. Adachi ◽

T. Morie ◽

K. Aihara

Keyword(s):

Time Series ◽

Dynamical Systems ◽

Discrete Time ◽

Time Series Data ◽

Series Data ◽

Electronic Circuits ◽

Bifurcation Diagrams ◽

Discrete Time Dynamical Systems

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A Tool to Explore Discrete-Time Data: The Time Series Response Analyser

International Journal of Sport Nutrition and Exercise Metabolism ◽

10.1123/ijsnem.2020-0150 ◽

2020 ◽

Vol 30 (5) ◽

pp. 374-381 ◽

Cited By ~ 2

Author(s):

Benjamin J. Narang ◽

Greg Atkinson ◽

Javier T. Gonzalez ◽

James A. Betts

Keyword(s):

Time Series ◽

Discrete Time ◽

Time Series Data ◽

Large Data ◽

Science Research ◽

Series Data ◽

Data Sets ◽

Time Data ◽

Short Commentary ◽

Computational Errors

The analysis of time series data is common in nutrition and metabolism research for quantifying the physiological responses to various stimuli. The reduction of many data from a time series into a summary statistic(s) can help quantify and communicate the overall response in a more straightforward way and in line with a specific hypothesis. Nevertheless, many summary statistics have been selected by various researchers, and some approaches are still complex. The time-intensive nature of such calculations can be a burden for especially large data sets and may, therefore, introduce computational errors, which are difficult to recognize and correct. In this short commentary, the authors introduce a newly developed tool that automates many of the processes commonly used by researchers for discrete time series analysis, with particular emphasis on how the tool may be implemented within nutrition and exercise science research.

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Inference of S-system Models of Genetic Networks from Noisy Time-series Data

Chem-Bio Informatics Journal ◽

10.1273/cbij.4.1 ◽

2004 ◽

Vol 4 (1) ◽

pp. 1-14 ◽

Cited By ~ 41

Author(s):

Shuhei Kimura ◽

Mariko Hatakeyama ◽

Akihiko Konagaya

Keyword(s):

Time Series ◽

Time Series Data ◽

Genetic Networks ◽

Series Data ◽

System Models ◽

Noisy Time Series

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

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

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

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

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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