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An Association Framework to Analyze Dependence Structure in Time Series

B. H. Fadlallah, A. J. Brockmeier, S. Seth, Lin Li, A. Keil, J. C. Principe
2012 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
The purpose of this paper is two-fold: first, to propose a modification to the generalized measure of association (GMA) framework that reduces the effect of temporal structure in time series; second, to  ...  assess the reliability of using association methods to capture dependence between pairs of EEG channels using their time series or envelopes.  ...  Therefore, for each realization in the time series, we dismiss the realizations within a neighboring time window to discard dependence purely pertaining to time structure.  ... 
doi:10.1109/embc.2012.6347404 pmid:23367339 dblp:conf/embc/FadlallahBSLKP12 fatcat:lhlc4hq26bcizietgwykxrybq4

Wavelet decomposition approach for understanding time-varying relationship of financial sector variables: a study of the indian stock market

Indranil Ghosh, IT & Analytics Area, Institute of Management Technology Hyderabad, Shamshabad, Hyderabad-501218, Telangana, India, Tamal Datta Chaudhuri, Centre for Knowledge, Ideas and Development Studies, KnIDS, Kolkata, India
2020 Multiple Criteria Decision Making  
The study uses wavelet decomposition framework for breaking down different financial time series into time-varying components.  ...  Quantile Regression, Wavelet Multiple Correlation and Cross-Correlation analysis, and Diebold-Yilmaz spillover analysis are then applied to investigate the nature of dependence, association, and spillover  ...  They further combined MODWT and DCC-GARCH in an integrated framework to successfully estimate hedge ratios across the time scales.  ... 
doi:10.22367/mcdm.2020.15.03 fatcat:lloss5bp3ncrrocyz2xvk4p2xm

Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions

Jason Liu, Daniel J. Spakowicz, Garrett I. Ash, Rebecca Hoyd, Rohan Ahluwalia, Andrew Zhang, Shaoke Lou, Donghoon Lee, Jing Zhang, Carolyn Presley, Ann Greene, Matthew Stults-Kolehmainen (+7 others)
2021 PLoS Computational Biology  
Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data.  ...  Specifically, we show how the framework is able to evaluate an exercise intervention's effect on stabilizing blood glucose in a diabetes dataset.  ...  In summary, the Bayesian structural time series framework was effective in determining the change for both of the participants analyzed.  ... 
doi:10.1371/journal.pcbi.1009303 pmid:34424894 pmcid:PMC8412351 fatcat:z6sirc46lrakrez4g3qtgmtqxm

Price graphs: Utilizing the structural information of financial time series for stock prediction [article]

Junran Wu, Ke Xu, Xueyuan Chen, Shangzhe Li, Jichang Zhao
2021 arXiv   pre-print
In this study, we propose a novel framework to address both issues. Specifically, in terms of transforming time series into complex networks, we convert market price series into graphs.  ...  Then, structural information, referring to associations among temporal points and the node weights, is extracted from the mapped graphs to resolve the problems regarding long-range dependencies and the  ...  In particular, the associated VG refines certain important features from the original time series, i.e., the structure within the time series [58] .  ... 
arXiv:2106.02522v5 fatcat:deumr3gphfbpzebe47ftxa3nki

Bayesian Structural Time Series for Mobile Health and Sensor Data: A Flexible Modeling Framework for Evaluating Interventions [article]

Jason Liu, Daniel J Spakowicz, Rebecca Hoyd, Garrett I Ash, Shaoke Lou, Donghoon Lee, Jing Zhang, Carolyn Presley, Ann Greene, Andrew V Papachristos, Mark Gerstein
2020 bioRxiv   pre-print
Here, we describe a general statistical framework for sensor and wearable data that applies a Bayesian structural time series model to analyze and understand various behavior and health data collected  ...  The Bayesian structural time series model shows robust performance in a wide variety of tasks, further supporting its applicability to current and future mobile health and sensor data types.  ...  This Bayesian structural time series framework can make use of complex covariate structures, which is useful and necessary to get an unbiased measure of impact.  ... 
doi:10.1101/2020.03.02.973677 fatcat:mb2tqm5375b3vntc2hgnmktefe

Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain

Sean L. Simpson, F. DuBois Bowman, Paul J. Laurienti
2013 Statistics Survey  
While the field of statistics has been integral in advancing activation analyses and some connectivity analyses in functional neuroimaging research, it has yet to play a commensurate role in complex network  ...  Here we survey widely used statistical and network science tools for analyzing fMRI network data and discuss the challenges faced in filling some of the remaining methodological gaps.  ...  An earlier version of this manuscript can be found at (Simpson et al., arXiv:1302.5721 [stat.ME])  ... 
doi:10.1214/13-ss103 pmid:25309643 pmcid:PMC4189131 fatcat:lyhvdaapwrb3llxtiehjo6lg3u

Assessing Causality Structures learned from Digital Text Media

Mariano Maisonnave, Fernando Delbianco, Fernando Tohmé, Ana G. Maguitman, Evangelos E. Milios
2020 Proceedings of the ACM Symposium on Document Engineering 2020  
The four analyzed methods are the pairwise Granger test, VAR(1), BigVar and SiMoNe. The framework is applied to the New York Times dataset, which covers news for a period of 246 months.  ...  In this paper we describe a framework to uncover potential causal relations between event mentions from streaming text of news media.  ...  ACKNOWLEDGEMENTS This research work was supported in part by CONICET (Argentina), a LARA Google Research grant, the Emerging Leaders in the Americas Program (ELAP-Canada) and Universidad Nacional del Sur  ... 
doi:10.1145/3395027.3419594 dblp:conf/doceng/MaisonnaveDTMM20 fatcat:d4wrfhj2gna5fic2fht5qv2sgy

Detection of Spatio-Temporal Recurrent Patterns in Dynamical Systems

Pietro Bonizzi, Ralf Peeters, Stef Zeemering, Arne van Hunnik, Olivier Meste, Joël Karel
2019 Frontiers in Applied Mathematics and Statistics  
It then becomes important to first detect whether repetitive spatio-temporal patterns are present, and if so, where they are located (both in space and time), to facilitate a focused RP analysis approach  ...  Results: A first simulation shows how the proposed framework can handle multiple recurrent patterns simultaneously occurring in a spatial structure of a dynamical system.  ...  Briefly, the time series associated with each point in the geometric structure are collected into a matrix B, which can be seen as a multi-variate time series.  ... 
doi:10.3389/fams.2019.00036 fatcat:iq34pjylsfhlblnngikbvwlbfe

Preemptive Prediction-Based Automated Cyberattack Framework Modeling

Sungwook Ryu, Jinsu Kim, Namje Park, Yongseok Seo
2021 Symmetry  
We propose an attack strategy prediction framework.  ...  by applying machine learning to the mapping of keywords frequently mentioned in attack strategies.  ...  It enumerates the association with the results of analysis of the existing network in a time series to analyze the trend of the association while analyzing the level of convergence of various subjects,  ... 
doi:10.3390/sym13050793 doaj:8bcd14eacc7f4cbfa2ca1d89e5fc2eec fatcat:73pdaoffundabpesbmaj54x4gq

Estimating patient's health state using latent structure inferred from clinical time series and text

Aaron Zalewski, William Long, Alistair E. W. Johnson, Roger G. Mark, Li-wei H. Lehman
2017 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)  
In particular, we employ HDP to combine clinical time series and text from the nursing progress notes in a probabilistic topic modeling framework for patient risk stratification.  ...  We evaluate the clinical utility of the learned topic structure using the first 24-hour ICU data from over 17,000 adult patients in the MIMIC-II database to estimate patients' risks of in-hospital mortality  ...  Acknowledgments The authors thank Eric Lehman for his contribution in deriving and evaluating time series features, Yang Dai for her assistance with data analysis, and Dr.  ... 
doi:10.1109/bhi.2017.7897302 pmid:28630952 pmcid:PMC5473944 fatcat:oa3qewmkaredfkznuasdhfdqny

Gradients of Connectivity as Graph Fourier Bases of Brain Activity [article]

Giulia Lioi, Vincent Gripon, Abdelbasset Brahim, François Rousseau, Nicolas Farrugia
2020 arXiv   pre-print
Despite the tremendous progress that has been achieved in this field, still relatively few methods exploit the topology of brain networks to analyze brain activity.  ...  The application of graph theory to model the complex structure and function of the brain has shed new light on its organization and function, prompting the emergence of network neuroscience.  ...  The authors investigated to what extent fMRI time-series are constrained by the underlying structure.  ... 
arXiv:2009.12567v1 fatcat:xpwfnqmolve2jonj5lkcix3htq

Incorporating climate trends in the stochastic modeling of extreme minimum air temperature series of Campinas, state of São Paulo, Brazil

Gabriel Constantino Blain
2011 Bragantia  
of the work was to describe the probabilistic structure of this series based on the general extreme value distribution (GEV) with parameters estimated as a function of a time covariate.  ...  However, since the parameters of location and scale of this probabilistic model are significantly conditioned on time, the presence of climate trends in the analyzed time series is proven.  ...  The MK final value (4.18) indicates the presence of a significant trend component in the analyzed time series since the p-value associated with this statistic is equal to 0.0052 (considerable lower than  ... 
doi:10.1590/s0006-87052011000400031 fatcat:ledgw57wendgritw6pylfudufa

Towards a conceptual framework for visual analytics of time and time-oriented data

Wolfgang Aigner, Alessio Bertone, Silvia Miksch, Christian Tominski, Heidrun Schumann
2007 2007 Winter Simulation Conference  
In this paper, we introduce a concept for designing visual analytics frameworks and tailored visual analytics systems for time and time-oriented data.  ...  This demands specialized methods in order to support proper analysis and visualization to explore trends, patterns, and relationships in different kinds of time-oriented data.  ...  The used internal data structures reflect this data model in terms of data structures to store and retrieve time-oriented data as well as associated metadata.  ... 
doi:10.1109/wsc.2007.4419666 dblp:conf/wsc/AignerBMTS07 fatcat:vftxy2ajjbhctp5dvwrelidpme

Analysis of velocity fluctuations and their intermittency properties in the surf zone using empirical mode decomposition

François G. Schmitt, Yongxiang Huang, Zhiming Lu, Yulu Liu, Nicolas Fernandez
2009 Journal of Marine Systems  
In order to characterize the intermittent properties of their fluctuations at many time scales, we analyze the experimental time series using the Empirical Mode Decomposition (EMD) method.  ...  We use the EMD approach in association with a newly developed method based on Hilbert spectral analysis, representing the probability density function in an amplitude-frequency space.  ...  Y.H. is financed in part by a Ph.D. grant from the French Ministry of Foreign Affairs. We thank Dominique Menu for the realisation of the metallic structure serving as support for the ADV.  ... 
doi:10.1016/j.jmarsys.2008.11.012 fatcat:qu3apshtafd7dhpfpez546xnui

Community Dynamics: Event and Role Analysis in Social Network Analysis [chapter]

Justin Fagnan, Reihaneh Rabbany, Mansoureh Takaffoli, Eric Verbeek, Osmar R. Zaïane
2014 Lecture Notes in Computer Science  
We discuss here an approach to analyzing community evolution events and entity role changes to uncover critical information in dynamic networks.  ...  By performing event analysis, the evolutions of communities are abstracted in order to see structure in the dynamic change over time.  ...  Community Dynamics Modelling In order to analyze dynamic social networks and study the evolution of their communities and individuals, we propose a two-stage framework, called MODEC, that analyzes the  ... 
doi:10.1007/978-3-319-14717-8_7 fatcat:qg5fm53hsnf27nqfdyeirwcjly
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