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Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series [article]

Feras A. Saad, Vikash K. Mansinghka
2018 arXiv   pre-print
This article proposes a Bayesian nonparametric method for forecasting, imputation, and clustering in sparsely observed, multivariate time series data.  ...  Given a collection of N time series, the Bayesian model first partitions them into independent clusters using a Chinese restaurant process prior.  ...  The authors wish to thank Max Orhai from Galois, Inc. for assembling the CDC flu dataset.  ... 
arXiv:1710.06900v2 fatcat:cxlskdx3bned3ncjdv3joi7lam

Page 441 of Mathematical Reviews Vol. , Issue 2004a [page]

2004 Mathematical Reviews  
Verboven, Robust PCA for high- dimensional data (169-179); Y. Kharin [Yurii Kharin], Robustness analysis in forecasting of time series (180-193); G. A. Koshevoy [G. A.  ...  Pires, Detection of outliers in multivari- ate data: a method based on clustering and robust estimators (291-296); Yeo-Hun Yoon, Young-Sang Kim, Sung-Jun Kim and Bong-Jin Yum, Development of a framework  ... 

How to Provide Accurate and Robust Traffic Forecasts Practically? [chapter]

Yang Zhang
2012 Intelligent Transportation Systems  
Under the circumstances, the method has the virtues of impartiality, robustness and a good "track-record" in time series forecasting.  ...  In time series, state is defined as a series of system values measured during the past k intervals (k N).  ...  How to reference In order to correctly reference this scholarly work, feel free to copy and paste the following: Yang Zhang (2012) . How to Provide Accurate and Robust Traffic Forecasts Practically?  ... 
doi:10.5772/27741 fatcat:iyjqbcy4rbap5fygyrl3wdms4q

A Bayesian approach to developing a strategic early warning system for the French milk market

Christophe Bisson, Furkan Gurpinar
2017 Journal of Intelligence Studies in Business  
We applied different multivariate time series regression and Bayesiannetworks on a pre-determined map of relations between macro-economic indicators.  ...  A new approach is provided in our paper for creating a strategic early warningsystem allowing the estimation of the future state of the milk market as scenarios.  ...  CONCLUSION We applied for the first time different multivariate time series regression and Bayesian networks to predict the impacting scenarios which are the heart of SEWS.  ... 
doi:10.37380/jisib.v7i3.277 fatcat:jmnppkgpo5hw3kpauz2u6voh4u

A sequential distance-based approach for imputing missing data: Forward Imputation

Nadia Solaro, Alessandro Barbiero, Giancarlo Manzi, Pier Alda Ferrari
2016 Advances in Data Analysis and Classification  
A Bayesian nonparametric approach is developed to approximate multivariate copulas in financial time series using Bernstein polynomials.  ...  C534: Bayesian nonparametric copulas for multivariate time series Presenter: Concepcion Ausin, Universidad Carlos III de Madrid, Spain Copula models have become a very common tool in financial time series  ... 
doi:10.1007/s11634-016-0243-0 fatcat:yvrqlgllsbesbnvnzzci2egpl4

Bayesian binary quantile regression for the analysis of Bachelor-to-Master transition

Cristina Mollica, Lea Petrella
2016 Journal of Applied Statistics  
methods for Predictive and Exploratory Path modeling  ...  The working group (WG) CMStatistics comprises a number of specialized teams in various research areas of computational and methodological statistics.  ...  To perform the analysis we developed a robust method for identifying and clustering bursts in sales and Twitter series based on their shape.  ... 
doi:10.1080/02664763.2016.1263835 fatcat:l5eyielgxrct7hq5ljqeej5ccy

Modelling the dynamic pattern of surface area in basketball and its effects on team performance

Rodolfo Metulini, Marica Manisera, Paola Zuccolotto
2018 Journal of Quantitative Analysis in Sports (JQAS)  
Using a time series of basketball players' coordinates, we focus on the dynamics of the surface area of the five players on the court with a two-fold purpose: (i) to give tools allowing a detailed description  ...  Because of the advent of GPS techniques, a wide range of scientific literature on Sport Science is nowadays devoted to the analysis of players' movement in relation to team performance in the context of  ...  analysis and forecasting that incorporates elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing.  ... 
doi:10.1515/jqas-2018-0041 fatcat:b3qwsi7tqjg2vdo7gbjtiorv6m

Mini-Workshop: Semiparametric Modelling of Multivariate Economic Time Series With Changing Dynamics

Luc Bauwens, Rainer von Sachs, Qiwei Yao
2010 Oberwolfach Reports  
This workshop aimed to bring together experts in this field in order to discuss recent methodology for multivariate time series dynamics which are changing over time: by an abrupt switch between two (or  ...  The emphasis has been on mathematical methods for semiparametric modelling and estimation, where "semiparametric" is to be understood in a rather broad sense: parametric models where the parameters are  ...  Third, [4] use a Bayesian approach to investigate the evidence for structural breaks in reduced form time-series models of realized volatility.  ... 
doi:10.4171/owr/2010/05 fatcat:n5mn6zzlnvfgdbgxu6vpri24i4

Comparison of Traffic Forecasting Methods in Urban and Suburban Context

Julien Salotti, Serge Fenet, Romain Billot, Nour-Eddin El Faouzi, Christine Solnon
2018 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)  
Linear and Time Series Methods • ARIMA [6] is a classical univariate time series model. It has been widely used in traffic flow forecasting literature.  ...  [15] used a dynamic Bayesian network to identify conditional independences and infer some causal drives embedded in traffic flow multivariate series. More recently, Runge et al.  ... 
doi:10.1109/ictai.2018.00132 dblp:conf/ictai/SalottiFBFS18 fatcat:zxehwc47zrhnnhsnw6ajsvt36q

A User-Friendly Guide to Multivariate Calibration and Classification

Eric R Ziegel
2004 Technometrics  
Competitors for this particular book are limited to books on logistic regression or generalized linear modeling, such as that by Myers, Montgomery, and Vining (2002) , which was reviewed for Technometrics  ...  BOOK REVIEWS All chapters begin with nice overviews and conclude with sections devoted to "Further Reading."  ...  The nal chapter, "Data Mining and Forecasting," is also new. It is concerned with mostly automatic methods for simultaneously doing forecasting for many different time series.  ... 
doi:10.1198/004017004000000167 fatcat:ibuh4viiebdhxcswvpzu2mt72q

Combined Survey Sampling Inference

Steven M Lalonde
2004 Technometrics  
Competitors for this particular book are limited to books on logistic regression or generalized linear modeling, such as that by Myers, Montgomery, and Vining (2002) , which was reviewed for Technometrics  ...  BOOK REVIEWS All chapters begin with nice overviews and conclude with sections devoted to "Further Reading."  ...  The nal chapter, "Data Mining and Forecasting," is also new. It is concerned with mostly automatic methods for simultaneously doing forecasting for many different time series.  ... 
doi:10.1198/tech.2004.s750 fatcat:qpilh36ovfgahpfia2l4hevl3a

Bayesian modeling and clustering for spatio-temporal areal data: an application to Italian unemployment [article]

Alexander Mozdzen, Andrea Cremaschi, Annalisa Cadonna, Alessandra Guglielmi, Gregor Kastner
2022 arXiv   pre-print
Spatio-temporal areal data can be seen as a collection of time series which are spatially correlated according to a specific neighboring structure.  ...  We cluster the areal units using a nonparametric prior, thereby learning latent partitions of the areal units.  ...  This implies a Bayesian nonparametric model for clustering the areal-specific time series of the unemployment rates.  ... 
arXiv:2206.10509v1 fatcat:vqhbkor67fa3hc344zpto2nm2a

ADAPTIVE MODELING AND FORECASTING OF NONLINEAR NONSTATIONARY PROCESSES

P. I. Bidyuk, V. M. Sineglazov
2020 Electronics and Control Systems  
The study is directed towards development of adaptive decision support system for modeling and forecasting nonlinear nonstationary processes in economy, finances and other areas of human activities.  ...  The uncertainties are inherent to data collecting, model constructing and forecasting procedures and play a role of negative influence factors to the information system computational procedures.  ...  The authors used the method of down-weighting older data based on the tuning parameter found as a result of minimizing mean square error of time series forecasts.  ... 
doi:10.18372/1990-5548.63.14515 fatcat:b4urcknhkrh77lvwx3kue4bh6u

Estimation of the covariance matrix of random effects in longitudinal studies

Yan Sun, Wenyang Zhang, Howell Tong
2007 Annals of Statistics  
The within cluster correlation structure plays a very important role in longitudinal data analysis.  ...  Motivated by data typified by a set from Bangladesh pertinent to the use of contraceptives, we propose a random effect varying-coefficient model, and an estimation procedure for the within cluster correlation  ...  Using a dataset on patients who suffered a stroke, we simulate missing assessments and compare the MVOP model to existing methods for imputing incomplete multivariate ordinal variables.  ... 
doi:10.1214/009053607000000523 fatcat:x6u3qclakrgr3bkryyxq4ooaaa

A Bayesian Nonparametric Latent Space Approach to Modeling Evolving Communities in Dynamic Networks

Joshua Daniel Loyal, Yuguo Chen
2022 Bayesian Analysis  
While methods for clustering with this approach exist for dynamic networks, they all assume a static community structure.  ...  This paper presents a Bayesian nonparametric model for dynamic networks that can model networks with evolving community structures.  ...  Acknowledgments The authors thank the associate editor and referees for their valuable comments and suggestions, which significantly improved this work.  ... 
doi:10.1214/21-ba1300 fatcat:txmnwhtnavfujpfuzeshw4rtra
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