scholarly journals The identification of temporal communities through trajectory clustering correlates with single-trial behavioural fluctuations in neuroimaging data

2019 ◽  
Author(s):  
William Hedley Thompson ◽  
Jessey Wright ◽  
James M. Shine ◽  
Russell A. Poldrack
Keyword(s):  
Time Series Data ◽  
Dynamic Network ◽  
Fmri Data ◽  
Series Data ◽  
Time Varying ◽  
Trajectory Clustering ◽  
Single Trial ◽  
Neuroimaging Data ◽  
New Perspective ◽  
Static Connectivity

AbstractInteracting sets of nodes and fluctuations in their interaction are important properties of a dynamic network system. In some cases the edges reflecting these interactions are directly quantifiable from the data collected. However, in many cases (such as functional magnetic resonance imaging (fMRI) data), the edges must be inferred from statistical relations between the nodes. Here we present a new method, Temporal Communities through Trajectory Clustering (TCTC), that derives time-varying communities directly from time-series data collected from the nodes in a network. First, we verify TCTC on resting and task fMRI data by showing that time-averaged results correspond with expected static connectivity results. We then show that the time-varying communities correlate and predict single-trial behaviour. This new perspective on temporal community detection of node-collected data identifies robust communities revealing ongoing spatiotemporal community configurations during task performance.

Visual Classification of Formula One Races by Events, Drivers, and Time Periods

2020 ◽  
pp. 101-114
Author(s):  
Tobias Lampprecht ◽  
David Salb ◽  
Marek Mauser ◽  
Huub van de Wetering ◽  
Michael Burch ◽  
...  
Keyword(s):  
Time Series Data ◽  
Series Data ◽  
Time Varying ◽  
Visual Metaphor ◽  
Web Based ◽  
Visual Classification ◽  
Formula One ◽  
Data Worth ◽  
Time Periods

Formula One races provide a wealth of data worth investigating. Although the time-varying data has a clear structure, it is pretty challenging to analyze it for further properties. Here the focus is on a visual classification for events, drivers, as well as time periods. As a first step, the Formula One data is visually encoded based on a line plot visual metaphor reflecting the dynamic lap times, and finally, a classification of the races based on the visual outcomes gained from these line plots is presented. The visualization tool is web-based and provides several interactively linked views on the data; however, it starts with a calendar-based overview representation. To illustrate the usefulness of the approach, the provided Formula One data from several years is visually explored while the races took place in different locations. The chapter discusses algorithmic, visual, and perceptual limitations that might occur during the visual classification of time-series data such as Formula One races.

scholarly journals Prediction of dynamic responses of floating structures using NARX with mirroring technique

2018 ◽  
Vol 203 ◽  
pp. 01025
Author(s):  
Ruly Irawan ◽  
Mohd Shahir Liew ◽  
Montasir Osman Ahmed Ali ◽  
Ahmad Mohamad Al Yacouby
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Dynamic Network ◽  
Dynamic Responses ◽  
Series Data ◽  
Ann Model ◽  
Floating Structure ◽  
Floating Structures ◽  
Time Series Modelling ◽  
Mirroring Technique

Displacements, velocities and accelerations of Six Degree of freedom of a single floating structure was predicted using Time Series NARX feedback neural Networks. The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic network, with feedback connections enclosing several layers of the network is based on the linear ARX model, which is commonly used in time-series modelling is used in this study. Time series data of displacements of a single floating structure was used for training and testing the ANN model. In the training stage, this time series data of environment parameters was used as input and dynamic responses was used as target. Benchmarking result and error prediction was compared between two techniques of Neural Network training. The prediction result of the model responses can be concluded that NARX with mirroring technique increase the accuracy and can be used to predict time series of dynamic responses of floating structures.

scholarly journals A Time-Varying Coupling Analysis of Expressway Traffic Volume and Manufacturing PMI

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shuo Sun ◽  
Mingchen Gu ◽  
Yingping Wang ◽  
Rongjie Lin ◽  
Lifeng Xing ◽  
...  
Keyword(s):  
Manufacturing Industry ◽  
Time Series Data ◽  
Traffic Volume ◽  
Series Data ◽  
Time Varying ◽  
Truck Traffic ◽  
Positive Interaction ◽  
Vector Autoregressive ◽  
Coupling Relationship ◽  
Johansen Cointegration Test

This study investigates the time-varying coupling relationship between expressway traffic volume and manufacturing purchasing manager index (PMI). First, for the traffic volume and manufacturing PMI time-series data, unit root stability test and Johansen cointegration test are applied to determine the stability of single sequence and the long-term stable correlation between variables, respectively. Then, a time-varying vector autoregressive model (TVP-VAR) is developed to quantify the time-varying correlation between variables. The time-varying parameters of TVP-VAR are estimated using the Markov chain Monte Carlo (MCMC) theory. Finally, the model is validated using examples from China. In the numeric example, three variables, i.e., expressway car traffic volume, expressway truck traffic volume, and manufacturing PMI, are selected for analysis. Results show that there is a positive interaction between expressway traffic volume (both car and truck) and manufacturing PMI. Express traffic volume slowly promotes the development of manufacturing industry. However, with the reform policy of road freight structure in China, the promotion effect of truck traffic on manufacturing PMI in the past two years has decreased significantly. Moreover, as affected by the China demand-led economic development model in recent years, the stimulus effect of manufacturing PMI on expressway passenger traffic volume has increased year by year. And, while the expressway freight structure remains stable, truck traffic volume is hardly affected by fluctuations in manufacturing PMI. These research results are helpful for policy makers to understand the time-varying coupling relationship between expressway traffic volume and manufacturing development and finally to improve the expressway management level.

scholarly journals A Longitudinal Model for Functional Connectivity Networks Using Resting-State fMRI

2017 ◽  
Author(s):  
Brian Hart ◽  
Ivor Cribben ◽  
Mark Fiecas ◽  
Keyword(s):  
Functional Connectivity ◽  
Time Series Data ◽  
Generalized Least Squares ◽  
Resting State Fmri ◽  
Testing Procedure ◽  
Fmri Data ◽  
Series Data ◽  
Group Differences ◽  
Permutation Testing ◽  
Novel Method

AbstractMany neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. However, the current network modeling literature lacks a general framework for analyzing functional connectivity (FC) networks in fMRI data obtained from a longitudinal study. In this work, we build a novel longitudinal FC network model using a variance components approach. First, for all subjects’ visits, we account for the autocorrelation inherent in the fMRI time series data using a non-parametric technique. Second, we use a generalized least squares approach to estimate 1) the within-subject variance component shared across the population, 2) the FC network, and 3) the FC network’s longitudinal trend. Our novel method for longitudinal FC networks seeks to account for the within-subject dependence across multiple visits, the variability due to the subjects being sampled from a population, and the autocorrelation present in fMRI data, while restricting the number of parameters in order to make the method computationally feasible and stable. We develop a permutation testing procedure to draw valid inference on group differences in baseline FC and change in FC over time between a set of patients and a comparable set of controls. To examine performance, we run a series of simulations and apply the model to longitudinal fMRI data collected from the Alzheimer’s Disease Neuroimaging Initiative database.

scholarly journals Linear Temporal Public Announcement Logic: a new perspective for reasoning the knowledge of multi-classifiers

2020 ◽  
Author(s):  
Amirhoshang Hoseinpour Dehkordi ◽  
Majid Alizadeh ◽  
Ali Movaghar
Keyword(s):  
Temporal Logic ◽  
Intelligent Systems ◽  
Time Series Data ◽  
Detection System ◽  
Transition System ◽  
Series Data ◽  
Performance Improvements ◽  
Public Announcement ◽  
Public Announcement Logic ◽  
New Perspective

Current applied intelligent systems have crucial shortcomings either in reasoning the gathered knowledge, or representation of comprehensive integrated information. To address these limitations, we develop a formal transition system which is applied to the common artificial intelligence (AI) systems, to reason about the findings. The developed model was created by combining the Public Announcement Logic (PAL) and the Linear Temporal Logic (LTL), which will be done to analyze both single-framed data and the following time-series data. To do this, first, the achieved knowledge by an AI-based system (i.e., classifiers) for an individual time-framed data, will be taken, and then, it would be modeled by a PAL. This leads to developing a unified representation of knowledge, and the smoothness in the integration of the gathered and external experiences. Therefore, the model could receive the classifier's predefined -or any external- knowledge, to assemble them in a unified manner. Alongside the PAL, all the timed knowledge changes will be modeled, using a temporal logic transition system. Later, following by the translation of natural language questions into the temporal formulas, the satisfaction leads the model to answer that question. This interpretation integrates the information of the recognized input data, rules, and knowledge. Finally, we suggest a mechanism to reduce the investigated paths for the performance improvements, which results in a partial correction for an object-detection system.

scholarly journals FINANCIAL CYCLES IN THE ECONOMY AND IN ECONOMIC RESEARCH: A CASE STUDY IN CHINA

2021 ◽  
Vol 27 (5) ◽  
pp. 1250-1279
Author(s):  
Yong Qin ◽  
Zeshui Xu ◽  
Xinxin Wang ◽  
Marinko Škare ◽  
Małgorzata Porada-Rochoń
Keyword(s):  
Time Series Data ◽  
Singular Spectrum Analysis ◽  
Real Life ◽  
Economic Research ◽  
Series Data ◽  
Time Varying ◽  
Causality Test ◽  
Vector Autoregression Model ◽  
Time Varying Parameter ◽  
Financial Cycles

This work explores the relationship between financial cycles in the economy and in economic research. To this aim, we take China as an empirical example, and an intuitive bibliometric analysis of selected terms concerning financial cycles in economic research is performed first. Both in the economy and in economic research, we then conduct singular spectrum analysis to further isolate and describe the specific length and amplitude of financial cycles for China based on quarterly time-series data. Finally, according to the estimated cycles that detrended by Hodrick-Prescott filter for financial and bibliometric variables, the Granger causality test scrutinizes the results of the first two steps. Moreover, a time-varying parameter vector autoregression model is estimated to quantitatively investigate the time-varying interaction between financial and bibliometric variables. Our study shows that financial cycles have a strong effect on the developments in the financial-related literature. In particular, the 2008 global financial crisis’s impulse intensity is significantly higher than in other periods. Surprisingly, discussions on financial cycles in the literature also have an impact on financial activities in real life. These findings contribute to nascent work on the patterns in financial cycles, thus providing a new and effective insight on the interpretation of financial activities.

Fusion Process Neural Networks Classifier Oriented Time Series

2019 ◽  
Vol 16 (10) ◽  
pp. 4059-4063
Author(s):  
Ge Li ◽  
Hu Jing ◽  
Chen Guangsheng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Feature Extraction ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Series Data ◽  
Time Varying ◽  
Synthetic Control ◽  
Classification Feature

Based on the consideration of complementary advantages, different wavelet, fractal and statistical methods are integrated to complete the classification feature extraction of time series. Combined with the advantage of process neural networks that processing time-varying information, we propose a fusion classifier with process neural network oriented time series. Be taking advantage of the multi-fractal processing nonlinear feature of time series data classification, the strong adaptability of the wavelet technique for time series data and the effect of statistical features on the classification of time series data, we can achieve the classification feature extraction of time series. Additionally, using time-varying input characteristics of process neural networks, the pattern matching of timevarying input information and space-time aggregation operation is realized. The feature extraction of time series with the above three methods is fused to the distance calculation between time-varying inputs and cluster space in process neural networks. We provide the process neural network fusion to the learning algorithm and optimize the calculation process of the time series classifier. Finally, we report the performance of our classification method using Synthetic Control Charts data from the UCI dataset and illustrate the advantage and validity of the proposed method.

scholarly journals Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data

2008 ◽  
Vol 24 (10) ◽  
pp. 1286-1292 ◽  
Author(s):  
Jongrae Kim ◽  
Declan G. Bates ◽  
Ian Postlethwaite ◽  
Pat Heslop-Harrison ◽  
Kwang-Hyun Cho
Keyword(s):  
Time Series ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Linear Time ◽  
Series Data ◽  
Multiple Time ◽  
Time Varying ◽  
Multiple Time Series ◽  
Linear Interactions ◽  
Time Varying Models

Privacy Preserving Data Mining Using Time Series Data Aggregation

2017 ◽  
Vol 8 (4) ◽  
pp. 1-15
Author(s):  
Sivaranjani Reddi
Keyword(s):  
Data Aggregation ◽  
Time Series Data ◽  
Cluster Head ◽  
Dynamic Network ◽  
Series Data ◽  
Distributed Network ◽  
Privacy Preserving Data Mining ◽  
Communication Costs ◽  
Network Feature ◽  
Network Functionality

This article proposes a mechanism to provide privacy to mined results by assuming that the data is distributed across many nodes. The first objective includes mining the query results by the node in a cluster, communicating it to the cluster head, aggregating the data collected from all the cluster nodes and then communicating it to the group controller. The second objective is to incorporate privacy at each level of the clusters node: cluster head and the group controller level. The final objective is to provide a dynamic network feature, where the nodes can join or leave the distributed network without disturbing the network functionality. The proposed algorithm was implemented and validated in Java for its performance in terms of communication costs computational complexity.

scholarly journals The macroeconomic determinants of technological progress in Nigeria

2011 ◽  
Vol 14 (3) ◽  
pp. 282-297 ◽  
Author(s):  
Olusegun Ayodele Akanbi
Keyword(s):  
Time Series ◽  
Technological Progress ◽  
Time Series Data ◽  
Series Data ◽  
Growth Theory ◽  
Time Varying ◽  
Factor Productivity ◽  
Endogenous Growth Theory ◽  
Macroeconomic Determinants ◽  
Douglas Production Function

This study empirically examines the macroeconomic determinants of technological progress (total factor productivity) in Nigeria that is consistent with the endogenous growth theory. The estimations are carried out with time-series data from 1970 to 2006 using the Johansen estimation techniques. The study is distinct from most of the existing literature since it made an attempt in generating a time-varying technological progress. It employs the Kalman filter technique to determine the evolution of the Solow residual estimated from a Cobb-Douglas production function. The results conform to the existing literature that macroeconomic instability, the level of financial development, and the level of human development are highly significant determinants of technological progress in Nigeria.

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