scholarly journals Learning the Structure of Graphical Models Based on Discrete Time Series Data in the Context of Ambient Assisted Living

2015 ◽  
Vol 25 (3-4) ◽  
Author(s):  
Sarah Fallmann ◽  
Johannes Kropf
Keyword(s):  
Time Series ◽  
Graphical Models ◽  
Discrete Time ◽  
Assisted Living ◽  
Time Series Data ◽  
Ambient Assisted Living ◽  
Series Data ◽  
Discrete Time Series

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

Biosystems ◽  
2008 ◽  
Vol 93 (3) ◽  
pp. 181-190 ◽  
Author(s):  
Markus Durzinsky ◽  
Annegret Wagler ◽  
Robert Weismantel ◽  
Wolfgang Marwan
Keyword(s):  
Time Series ◽  
Discrete Time ◽  
Time Series Data ◽  
Genetic Networks ◽  
Series Data ◽  
Discrete Time Series

scholarly journals Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network of Caenorhabditis elegans

2018 ◽  
Vol 373 (1758) ◽  
pp. 20170377 ◽  
Author(s):  
Hexuan Liu ◽  
Jimin Kim ◽  
Eli Shlizerman
Keyword(s):  
Time Series ◽  
Nervous System ◽  
Caenorhabditis Elegans ◽  
Graphical Models ◽  
Neuronal Network ◽  
Graphical Model ◽  
Time Series Data ◽  
Series Data ◽  
Neuronal Responses ◽  
C Elegans

We propose an approach to represent neuronal network dynamics as a probabilistic graphical model (PGM). To construct the PGM, we collect time series of neuronal responses produced by the neuronal network and use singular value decomposition to obtain a low-dimensional projection of the time-series data. We then extract dominant patterns from the projections to get pairwise dependency information and create a graphical model for the full network. The outcome model is a functional connectome that captures how stimuli propagate through the network and thus represents causal dependencies between neurons and stimuli. We apply our methodology to a model of the Caenorhabditis elegans somatic nervous system to validate and show an example of our approach. The structure and dynamics of the C. elegans nervous system are well studied and a model that generates neuronal responses is available. The resulting PGM enables us to obtain and verify underlying neuronal pathways for known behavioural scenarios and detect possible pathways for novel scenarios. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.

scholarly journals Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London

2008 ◽  
Vol 5 (25) ◽  
pp. 885-897 ◽  
Author(s):  
Simon Cauchemez ◽  
Neil M Ferguson
Keyword(s):  
Time Series ◽  
Discrete Time ◽  
Continuous Time ◽  
Generation Time ◽  
Data Augmentation ◽  
Time Series Data ◽  
Observation Interval ◽  
Series Data ◽  
Time Model ◽  
Similar Time

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.

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

2020 ◽  
Vol 30 (5) ◽  
pp. 374-381 ◽  
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.

scholarly journals Psychometric network models from time-series and panel data

2019 ◽  
Author(s):  
Sacha Epskamp
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Graphical Models ◽  
Latent Variables ◽  
Large Scale ◽  
Time Series Data ◽  
Series Data ◽  
Single Subject ◽  
Time Interval ◽  
Cross Sectional

Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGM)---an undirected network model of partial correlations---between observed variables of cross-sectional data or single subject time-series data. This assumes that all variables are measured without measurement error, which may be implausible. In addition, cross-sectional data cannot distinguish between within-subject and between-subject effects. This paper provides a general framework that extends GGM modeling with latent variables, including relationships over time. These relationships can be estimated from time-series data or panel data featuring at least three waves of measurement. The model takes the form of a graphical vector-autoregression model between latent variables and is termed the ts-lvgvar when estimated from time-series data and the panel-lvgvar when estimated from panel data. These methods have been implemented in the software package psychonetrics, which is exemplified in two empirical examples, one using time-series data and one using panel data, and evaluated in two large-scale simulation studies. The paper concludes with a discussion on ergodicity and generalizability. Although within-subject effects may in principle be separated from between-subject effects, the interpretation of these results rest on the intensity and the time interval of measurement and on the plausibility of the assumption of stationarity.

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
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