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Statistical Learning for Big Dependent Data (Wiley Series in Probability and Statistics) - Hardcover
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- VerlagWiley-Blackwell
- Erscheinungsdatum2021
- ISBN 10 1119417384
- ISBN 13 9781119417385
- EinbandTapa dura
- Anzahl der Seiten560
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Statistical Learning for Big Dependent Data (Hardcover)
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John Wiley & Sons Inc, New York
(2021)
ISBN 10: 1119417384
ISBN 13: 9781119417385
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Buchbeschreibung Hardcover. Zustand: new. Hardcover. Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time seriesAn automatic procedure to build univariate ARMA models for individual components of a large data setPowerful outlier detection procedures for large sets of related time seriesNew methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time seriesBroad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor modelsDiscussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time seriesForecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting.Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9781119417385
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Analysis of Big Dependent Data (Wiley Series in Probability and Statistics) by Peña, Daniel, Tsay, Ruey S. [Hardcover ]
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Statistical Learning for Big Dependent Data Format: Cloth
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Statistical Learning for Big Dependent Data
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Analysis of Big Dependent Data
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Wiley 2021-06-11, Hoboken
(2021)
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Buchbeschreibung hardback. Zustand: New. Language: ENG. Bestandsnummer des Verkäufers 9781119417385
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Statistical Learning for Big Dependent Data Wiley Series in Probability and Statistics
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Buchbeschreibung HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000. Bestandsnummer des Verkäufers FW-9781119417385
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Statistical Learning for Big Dependent Data (Wiley Series in Probability and Statistics)
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Statistical Learning for Big Dependent Data (Hardcover)
Verlag:
John Wiley & Sons Inc, New York
(2021)
ISBN 10: 1119417384
ISBN 13: 9781119417385
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Buchbeschreibung Hardcover. Zustand: new. Hardcover. Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time seriesAn automatic procedure to build univariate ARMA models for individual components of a large data setPowerful outlier detection procedures for large sets of related time seriesNew methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time seriesBroad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor modelsDiscussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time seriesForecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting.Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9781119417385
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Statistical Learning for Big Dependent Data (Hardcover)
Verlag:
John Wiley & Sons Inc, New York
(2021)
ISBN 10: 1119417384
ISBN 13: 9781119417385
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Hardcover
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Buchbeschreibung Hardcover. Zustand: new. Hardcover. Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time seriesAn automatic procedure to build univariate ARMA models for individual components of a large data setPowerful outlier detection procedures for large sets of related time seriesNew methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time seriesBroad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor modelsDiscussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time seriesForecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting.Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9781119417385
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