scholarly journals Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections

2018 ◽  
Vol 6 (3) ◽  
pp. 416-427 ◽  
Author(s):  
Sacha Epskamp ◽  
Claudia D. van Borkulo ◽  
Date C. van der Veen ◽  
Michelle N. Servaas ◽  
Adela-Maria Isvoranu ◽  
...  
Keyword(s):  
Time Series ◽  
Partial Correlation ◽  
Time Series Data ◽  
Recent Literature ◽  
Series Data ◽  
Network Structures ◽  
Cross Sectional ◽  
Correlation Networks ◽  
Relevant Variables ◽  
Analysis Of Time Series

Recent literature has introduced (a) the network perspective to psychology and (b) collection of time series data to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intraindividual network structures. We argue that these networks can be directly applied in clinical research and practice as hypothesis generating structures. Two networks can be computed: a temporal network, in which one investigates if symptoms (or other relevant variables) predict one another over time, and a contemporaneous network, in which one investigates if symptoms predict one another in the same window of measurement. The contemporaneous network is a partial correlation network, which is emerging in the analysis of cross-sectional data but is not yet utilized in the analysis of time series data. We explain the importance of partial correlation networks and exemplify the network structures on time series data of a psychiatric patient.

scholarly journals Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections

2017 ◽  
Author(s):  
Sacha Epskamp ◽  
Claudia van Borkulo ◽  
Date C. van der Veen ◽  
Michelle Servaas ◽  
Adela-Maria Isvoranu ◽  
...  
Keyword(s):  
Time Series ◽  
Partial Correlation ◽  
Time Series Data ◽  
Recent Literature ◽  
Series Data ◽  
Network Structures ◽  
Cross Sectional ◽  
Correlation Networks ◽  
Relevant Variables ◽  
Analysis Of Time Series

Recent literature has introduced (1) the network perspective to psychology, and (2) collection of time-series data in order to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intra-individual network structures. We argue that these networks can be directly applied in clinical research and practice as hypothesis generating structures. Two networks can be computed: a temporal network, in which one investigates if symptoms (or other relevant variables) predict one another over time, and a contemporaneous network, in which one investigates if symptoms predict one another in the same window of measurement. The contemporaneous network is a partial correlation network, which is emerging in the analysis of cross-sectional data but is not yet utilized in the analysis of time-series data. We explain the importance of partial correlation networks and exemplify the network structures on time-series data of a psychiatric patient.

An easy way to create duration variables in binary cross-sectional time-series data

2020 ◽  
Vol 20 (4) ◽  
pp. 916-930
Author(s):  
Andrew Q. Philips
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Series Data ◽  
Series Data ◽  
Duration Dependence ◽  
Cross Sectional ◽  
Common Solution ◽  
The Common

In cross-sectional time-series data with a dichotomous dependent variable, failing to account for duration dependence when it exists can lead to faulty inferences. A common solution is to include duration dummies, polynomials, or splines to proxy for duration dependence. Because creating these is not easy for the common practitioner, I introduce a new command, mkduration, that is a straightforward way to generate a duration variable for binary cross-sectional time-series data in Stata. mkduration can handle various forms of missing data and allows the duration variable to easily be turned into common parametric and nonparametric approximations.

Dimensionality Reduction for the Analysis of Time Series Data from Wind Turbines

2017 ◽  
pp. 317-339 ◽  
Author(s):  
Jochen Garcke ◽  
Rodrigo Iza-Teran ◽  
Marvin Marks ◽  
Mandar Pathare ◽  
Dirk Schollbach ◽  
...  
Keyword(s):  
Time Series ◽  
Dimensionality Reduction ◽  
Wind Turbines ◽  
Time Series Data ◽  
Series Data ◽  
Analysis Of Time Series

Influence of Inclination Angle on Intermittent Two Phase Flows

2016 ◽  
Author(s):  
Josep Escrig Escrig ◽  
Buddhika Hewakandamby ◽  
Georgios Dimitrakis ◽  
Barry Azzopardi
Keyword(s):  
Time Series ◽  
Void Fraction ◽  
Time Series Data ◽  
Silicone Oil ◽  
Wire Mesh ◽  
Series Data ◽  
Intermittent Flow ◽  
Two Phase ◽  
Cross Sectional ◽  
Inclined Pipes

Intermittent gas and liquid two-phase flow was generated in a 6 m × 67 mm diameter pipe mounted rotatable frame (vertical up to −20°). Air and a 5 mPa s silicone oil at atmospheric pressure were studied. Gas superficial velocities between 0.17 and 2.9 m/s and liquid superficial velocities between 0.023 and 0.47 m/s were employed. These runs were repeated at 7 angles making a total of 420 runs. Cross sectional void fraction time series were measured over 60 seconds for each run using a Wire Mesh Sensor and a twin plane Electrical Capacitance Tomography. The void fraction time series data were analysed in order to extract average void fraction, structure velocities and structure frequencies. Results are presented to illustrate the effect of the angle as well as the phase superficial velocities affect the intermittent flows behaviour. Existing correlations suggested to predict average void fraction and gas structures velocity and frequency in slug flow have been compared with new experimental results for any intermittent flow including: slug, cap bubble and churn. Good agreements have been seen for the gas structure velocity and mean void fraction. On the other hand, no correlation was found to predict the gas structure frequency, especially in vertical and inclined pipes.

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