scholarly journals Equilibria in Personality States: A Conceptual Primer for Dynamical Analyses

2019 ◽  
Author(s):  
Alexander Francois Danvers ◽  
Richard Wundrack ◽  
Matthias R. Mehl
Keyword(s):  
Dynamic Systems ◽  
Time Series Data ◽  
Systems Approach ◽  
Equilibrium Points ◽  
Personality Development ◽  
Series Data ◽  
Basic Step ◽  
Systems Model ◽  
Highly Correlated ◽  
Using Data

We provide a basic, step-by-step introduction to the core concepts and mathematical fundamentals of dynamic systems modeling through applying the Change as Outcome model, a simple dynamical systems model, to personality state data. This model characterizes changes in personality states with respect to equilibrium points, estimating attractors and their strength in time series data. Using data from the Personality and Interpersonal Roles (PAIRS) study, we find that mean state is highly correlated with attractor position but weakly correlated with attractor strength, suggesting strength provides added information not captured by summaries of the distribution. We then discuss how taking a dynamic systems approach to personality states also entails a theoretical shift. Instead of emphasizing partitioning trait and state variance, dynamic systems analyses of personality states emphasize characterizing patterns generated by mutual, ongoing interactions. Change as outcome modeling also allows for the effects of personality development after significant life changes to be conceptualized in more nuanced ways, separating effects on characteristic states after the significant change and how people are drawn towards those states (an aspect of resiliency). Estimating this model demonstrates core dynamics principles and provides quantitative grounding for measures of “repulsive” personality states and “ambivert” personality structures. Supplementary materials: https://osf.io/dps4w.

scholarly journals Equilibria in Personality States: A Conceptual Primer for Dynamics in Personality States

2020 ◽  
Vol 34 (6) ◽  
pp. 999-1016 ◽  
Author(s):  
Alexander F. Danvers ◽  
Richard Wundrack ◽  
Matthias Mehl
Keyword(s):  
Dynamic Systems ◽  
Time Series Data ◽  
Systems Approach ◽  
Equilibrium Points ◽  
Personality Development ◽  
Series Data ◽  
Basic Step ◽  
Personality Psychology ◽  
Systems Model ◽  
Highly Correlated

We provide a basic, step–by–step introduction to the core concepts and mathematical fundamentals of dynamic systems modelling through applying the Change as Outcome model, a simple dynamical systems model, to personality state data. This model characterizes changes in personality states with respect to equilibrium points, estimating attractors and their strength in time series data. Using data from the Personality and Interpersonal Roles study, we find that mean state is highly correlated with attractor position but weakly correlated with attractor strength, suggesting strength provides added information not captured by summaries of the distribution. We then discuss how taking a dynamic systems approach to personality states also entails a theoretical shift. Instead of emphasizing partitioning trait and state variance, dynamic systems analyses of personality states emphasize characterizing patterns generated by mutual, ongoing interactions. Change as Outcome modelling also allows for estimating nuanced effects of personality development after significant life changes, separating effects on characteristic states after the significant change and how strongly she or he is drawn towards those states (an aspect of resiliency). Estimating this model demonstrates core dynamics principles and provides quantitative grounding for measures of ‘repulsive’ personality states and ‘ambivert’ personality structures. © 2020 European Association of Personality Psychology

A Dynamic Dyadic Systems Approach to Interpersonal Communication

2021 ◽  
Author(s):  
Denise Haunani Solomon ◽  
Miriam Brinberg ◽  
Graham D Bodie ◽  
Susanne Jones ◽  
Nilam Ram
Keyword(s):  
Interpersonal Communication ◽  
Systems Analysis ◽  
Time Series Data ◽  
Systems Approach ◽  
Dynamic Structure ◽  
Relevant Information ◽  
Dyadic Interaction ◽  
Series Data ◽  
Conversational Units ◽  
Dyadic Systems

Abstract This article articulates conceptual and methodological strategies for studying the dynamic structure of dyadic interaction revealed by the turn-to-turn exchange of messages between partners. Using dyadic time series data that capture partners’ back-and-forth contributions to conversations, dynamic dyadic systems analysis illuminates how individuals act and react to each other as they jointly construct conversations. Five layers of inquiry are offered, each of which yields theoretically relevant information: (a) identifying the individual moves and dyadic spaces that set the stage for dyadic interaction; (b) summarizing conversational units and sequences; (c) examining between-dyad differences in overall conversational structure; (d) describing the temporal evolution of conversational units and sequences; and (e) mapping within-dyad dynamics of conversations and between-dyad differences in those dynamics. Each layer of analysis is illustrated using examples from research on supportive conversations, and the application of dynamic dyadic systems analysis to a range of interpersonal communication phenomena is discussed.

Model Validation of Dynamic Engineering Models Under Uncertainty

2016 ◽  
Author(s):  
Zequn Wang ◽  
Yan Fu ◽  
Ren-Jye Yang ◽  
Saeed Barbat ◽  
Wei Chen
Keyword(s):  
Dynamic Model ◽  
Model Validation ◽  
Dynamic Systems ◽  
Time Series Data ◽  
Integral Transform ◽  
Random Coefficients ◽  
Series Data ◽  
Input Space ◽  
Eigen Analysis ◽  
Engineering Models

Validating dynamic engineering models is critically important in practical applications by assessing the agreement between simulation results and experimental observations. Though significant progresses have been made, the existing metrics lack the capability of managing uncertainty in both simulations and experiments, which may stem from computer model instability, imperfection in material fabrication and manufacturing process, and variations in experimental conditions. In addition, it is challenging to validate a dynamic model aggregately over both the time domain and a model input space with data at multiple validation sites. To overcome these difficulties, this paper presents an area-based metric to systemically handle uncertainty and validate computational models for dynamic systems over an input space by simultaneously integrating the information from multiple validation sites. To manage the complexity associated with a high-dimensional data space, Eigen analysis is performed for the time series data from simulations at each validation site to extract the important features. A truncated Karhunen-Loève (KL) expansion is then constructed to represent the responses of dynamic systems, resulting in a set of uncorrelated random coefficients with unit variance. With the development of a hierarchical data fusion strategy, probability integral transform is then employed to pool all the resulting random coefficients from multiple validation sites across the input space into a single aggregated metric. The dynamic model is thus validated by calculating the cumulative area difference of the cumulative density functions. The proposed model validation metric for dynamic systems is illustrated with a mathematical example, a supported beam problem with stochastic loads, and real data from the vehicle occupant restraint system.

scholarly journals Forecasting the Spread of COVID-19 Pandemic with Prophet

2021 ◽  
Vol 35 (2) ◽  
pp. 115-122
Author(s):  
Mohan Mahanty ◽  
K. Swathi ◽  
K. Sasi Teja ◽  
P. Hemanth Kumar ◽  
A. Sravani
Keyword(s):  
Data Science ◽  
Statistical Data ◽  
Time Series Data ◽  
Daily Basis ◽  
Series Data ◽  
Use Case ◽  
The Past ◽  
Open Source Framework ◽  
Forecast Models ◽  
Using Data

COVID-19 pandemic shook the whole world with its brutality, and the spread has been still rising on a daily basis, causing many nations to suffer seriously. This paper presents a medical stance on research studies of COVID-19, wherein we estimated a time-series data-based statistical model using prophet to comprehend the trend of the current pandemic in the coming future after July 29, 2020 by using data at a global level. Prophet is an open-source framework discovered by the Data Science team at Facebook for carrying out forecasting based operations. It aids to automate the procedure of developing accurate forecasts and can be customized according to the use case we are solving. The Prophet model is easy to work because the official repository of prophet is live on GitHub and is open for contributions and can be fitted effortlessly. The statistical data presented on the paper refers to the number of daily confirmed cases officially for the period January 22, 2020, to July 29, 2020. The estimated data produced by the forecast models can then be used by Governments and medical care departments of various countries to manage the existing situation, thus trying to flatten the curve in various nations as we believe that there is minimal time to do this. The inferences made using the model can be clearly comprehended without much effort. Furthermore, it tries to give an understanding of the past, present, and future trends by showing graphical forecasts and statistics. Compared to other models, prophet specifically holds its own importance and innovativeness as the model is fully automated and generates quick and precise forecasts that can be tunable additionally.

scholarly journals Detection of Outliers in the Volatility of Malaysia Shariah Compliant Index Return: The Impulse Indicator Saturation Approach

2020 ◽  
pp. 1-7
Author(s):  
Ida Normaya Mohd Nasir ◽  
Mohd Tahir Ismail
Keyword(s):  
Time Series Data ◽  
Global Financial Crisis ◽  
Financial Time Series ◽  
The United States ◽  
Series Data ◽  
Crude Oil Prices ◽  
Global Events ◽  
Using Data ◽  
Impulse Indicator Saturation ◽  
Detection Of Outliers

Financial time series data often affected by various unexpected events which known as the outliers. The aim of this study is to detect the outliers in high frequency data using Impulse Indicator Saturation approach (IIS).Monte Carlo simulations illustrate the ability of IIS to detect outliers by using data with various simulation settings. For empirical application, we have chosen the Malaysia Shariah compliant index which is the FBM EMAS Shariah (FBMS) index. The result of this study discovered the presence of 47 outliers which related to several global events such as global financial crisis (2008 & 2009), the falling of stock market (2011), the United States debt-ceiling crisis (2013) and the declination of international crude oil prices (2014). Keywords: outliers; volatility; stock indices; IIS

scholarly journals Options for calibrating CERES-maize genotype specific parameters under data-scarce environments

2018 ◽  
Author(s):  
A.A Adnan ◽  
J. Diels ◽  
J.M. Jibrin ◽  
A.Y. Kamara ◽  
P. Craufurd ◽  
...  
Keyword(s):  
Field Experiments ◽  
Time Series Data ◽  
Simulation Models ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Planting Dates ◽  
Maize Varieties ◽  
Using Data ◽  
Set Up ◽  
Nitrogen Contents

AbstractMost crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect of most modelling exercises because it requires expensive and time-consuming field experiments. GSPs could also be estimated using multi-year and multi locational data from breeder evaluation experiments. This research was set up with the following objectives: i) to determine GSPs of 10 newly released maize varieties for the Nigerian Savannas using data from both calibration experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. Breeder evaluation data was also collected for 2 years and 7 locations. The calibrated GSPs were evaluated using data from a 4 year experiment conducted under varying nitrogen rates (0, 60 and 120kg N ha−1). For the model calibration using experimental data, calculated model efficiency (EF) values ranged between 0.86-0.92 and coefficient of determination (d-index) between 0.92-0.98. Calibration of time-series data produced nRMSE below 7% while all prediction deviations were below 10% of the mean. For breeder experiments, EF (0.52-0.81) and d-index (0.46-0.83) ranges were lower. Prediction deviations were below 17% of the means for all measured variables. Model evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen contents. We conclude that higher calibration accuracy of CERES-Maize model is achieved from detailed experiments. If unavailable, data from breeder experimental trials collected from many locations and planting dates can be used with lower but acceptable accuracy.

scholarly journals Relationship between Foreign Exchange Rate and Stock Price of Commercial Joint Stock Banks: Evidence from Vietnam

2016 ◽  
Vol 8 (7) ◽  
pp. 193 ◽  
Author(s):  
Tran Mong Uyen Ngan
Keyword(s):  
Exchange Rate ◽  
Foreign Exchange ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Foreign Exchange Rate ◽  
Series Data ◽  
Causality Test ◽  
Using Data ◽  
The Relationship

The relationship between foreign exchange rate and stock price is one popular topic that is interested by not only board managers of banks but also stock investors. By using data about foreign exchange rate between Vietnam Dong (VND) and United State Dollar (USD), stock prices data of nine commercial joint stock banks in Vietnam from the first day of 2013 to the last day of 2015, this paper try to answer the question “Does foreign exchange rate impact on stock price and vice verse?”. Applying Dickey Fuller test and Var Granger Causality test for the time series data, the results show that there is an impact of foreign exchange rate on stock price. Although the fluctuation in foreign exchange rate VND/USD causes the change in stock prices of commercial joint stock banks in Vietnam, however, the vector of this impact is not clearly. On the opposite way, the change in stock price does not cause the change in foreign exchange rate, this relation is one-way relation.

Updating the dual systems model of temporal cognition: Reasoning with dynamic systems theory

2019 ◽  
Vol 42 ◽  
Author(s):  
Annette Hohenberger
Keyword(s):  
Systems Theory ◽  
Time Scales ◽  
Dynamic Systems ◽  
Systems Approach ◽  
Temporal Reasoning ◽  
Dynamic Systems Theory ◽  
Dual Systems ◽  
Temporal Cognition ◽  
Systems Model ◽  
Two Systems

Abstract This commentary construes the relation between the two systems of temporal updating and temporal reasoning as a bifurcation and tracks it across three time scales: phylogeny, ontogeny, and microgeny. In taking a dynamic systems approach, flexibility, as mentioned by Hoerl & McCormack, is revealed as the key characteristic of human temporal cognition.

scholarly journals Enhanced accuracy of rainfall–runoff modeling with wavelet transform

2012 ◽  
Vol 15 (2) ◽  
pp. 392-404 ◽  
Author(s):  
Chien-ming Chou
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Response Function ◽  
Time Series Data ◽  
Series Data ◽  
Rainfall Time Series ◽  
Calibration Data ◽  
Validation Data ◽  
Original Scale ◽  
Using Data

Wavelet transform (WT) is typically used to decompose time series data for only one hydrological feature at a time. This study applied WT for simultaneous decomposition of rainfall and runoff time series data. For the calibration data, the decomposed rainfall and runoff time series calibrate the subsystem response function using the least squares (LS) method at each scale. For the validation data, the decomposed rainfall time series are convoluted with the estimated subsystem response function to obtain the estimated runoff at each scale. The estimated runoff at the original scale can be obtained by wavelet reconstruction. The efficacy of the proposed method is evaluated in two case studies of the Feng-Hua Bridge and Wu-Tu watershed. The analytic results confirm that the proposed wavelet-based method slightly outperforms the conventional method of using data only at the original scale. The results also show that the runoff hydrograph estimated by using the proposed method is smoother than that obtained using a single scale.

Modelling rein tension during riding sessions using the generalised additive modelling technique

2018 ◽  
Vol 14 (4) ◽  
pp. 209-221 ◽  
Author(s):  
A. Egenvall ◽  
A. Byström ◽  
L. Roepstorff ◽  
M. Rhodin ◽  
M. Eisersiö ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Stride Length ◽  
Time Series Data ◽  
Training Session ◽  
Series Data ◽  
Video Recordings ◽  
Exploratory Technique ◽  
Tension Curve ◽  
Using Data ◽  
The Right

General additive modelling (GAM-modelling) is an exploratory technique that can be used on longitudinal (time series) data, e.g. rein tension, over a period of time. The aim was to apply GAM-modelling to investigate changes in rein tension during a normal flatwork training session. Six riders each rode two or three of their horses (n=17 horses) during a normal flatwork/dressage training session with video recordings and rein tension measurements (128 Hz). Training sessions were classified according to rider position, stride length and whether horses were straight, bent to the left or bent to the right. The rein tension data were split into strides and for each stride minimal (MIN) and maximal (MAX) rein tension were determined and the area under the rein tension curve (AUC) was calculated. Using data on a contact the three outcome variables MIN, MAX and AUC rein tension were modelled by horse and rein (left/right), and time within the session was modelled as a smooth function. Two additional sets of models were constructed; one set using data within-rein with gait as a fixed effect and one set with rein and gait as fixed effects. Mean ± standard deviation values were MIN: 8.0±7.7 N, AUC: 180±109 Ns, and MAX: 49±31 N. GAM-modelling extracted visually interpretable information from the originally chaotic rein tension signals. Modelled data suggest that MIN, AUC and MAX follow the same pattern within horse. In general, rein tension was lowest in walk, intermediate in trot and highest in canter. Evaluating the entire ride, 12/17 horses systematically showed higher tension in the right rein. It is concluded that GAM-models may be useful for detecting patterns through time in biomechanical data.

Sign in / Sign up

Export Citation Format

Share Document