LEADER 00000cam a2200697 i 4500 003 OCoLC 005 20240129213017.0 006 m o d 007 cr cnu---unuuu 008 200815t20212021nju ob 001 0 eng 010 2020026631 019 1266651507 020 9781119417408|qelectronic book 020 1119417406|qelectronic book 020 9781119417392|qelectronic book 020 1119417392|qelectronic book 020 9781119417415|qelectronic book 020 1119417414|qelectronic book 020 |qhardcover 029 1 AU@|b000067928953 029 1 AU@|b000070668077 035 (OCoLC)1193558110|z(OCoLC)1266651507 037 9781119417385|bO'Reilly Media 040 DLC|beng|erda|cDLC|dOCLCO|dOCLCF|dDG1|dOCLCO|dUKAHL|dYDX |dORMDA|dOCLCO|dOCLCQ|dOCLCO|dOCLCL 042 pcc 049 INap 082 00 005.7 082 00 005.7|223 099 eBook O'Reilly for Public Libraries 100 1 Peña, Daniel,|d1948-|eauthor. 245 10 Statistical learning for big dependent data /|cDaniel Peña , Ruey S. Tsay.|h[O'Reilly electronic resource] 250 First edition. 264 1 Hoboken, NJ :|bJohn Wiley & Sons, Inc.,|c2021. 264 4 |c©2021 300 1 online resource. 336 text|btxt|2rdacontent 337 computer|bc|2rdamedia 338 online resource|bcr|2rdacarrier 490 1 Wiley series in probability and statistics 504 Includes bibliographical references and index. 505 0 Introduction to big dependent data -- Linear univariate time series -- Analysis of multivariate time series -- Handling heterogeneity in many time series -- Clustering and classification of time series -- Dynamic factor models -- Forecasting with big dependent data -- Machine learning of big dependent data -- Spatio-temporal dependent data. 520 "This book presents methods useful for analyzing and understanding large data sets that are dynamically dependent. The book will begin with examples of multivariate dependent data and tools for presenting descriptive statistics of such data. It then introduces some useful statistical methods for univariate time series analysis emphasizing on statistical procedures for modeling and forecasting. Both linear and nonlinear models are discussed. Special attention is given to analysis of high-frequency dependent data. The second part of the book considers joint dependency, both contemporaneous and dynamical dependence, among multiple series of dependent data. Special attention will be given to graphical methods for large data, to handling heterogeneity in time series (such as outliers, missing values, and changes in the covariance matrices), and to time-varying parameters for multivariate time series. The third part of the book is devoted to analysis of high-dimensional dependent data. The focus is on topics that are useful when the number of time series is large. The selected topics include clustering time series, high-dimensional linear regression for dependent data and its applications, and reducing the dimension with dynamic principal components and factor models. Throughout the book, advantages and disadvantages of the methods discussed are given and real examples are used in demonstration. The book will be of interest to graduate students, researchers, and practitioners in business, economics, engineering, and science who are interested in statistical methods for analyzing big dependent data and forecasting"--|cProvided by publisher. 588 Description based on online resource; title from digital title page (viewed on July 08, 2021). 590 O'Reilly|bO'Reilly Online Learning: Academic/Public Library Edition 650 0 Big data|xMathematics. 650 0 Time-series analysis. 650 0 Data mining|xStatistical methods. 650 0 Forecasting|xStatistical methods. 650 6 Données volumineuses|xMathématiques. 650 6 Série chronologique. 650 6 Prévision|xMéthodes statistiques. 650 7 Data mining|xStatistical methods|2fast 650 7 Forecasting|xStatistical methods|2fast 650 7 Time-series analysis|2fast 700 1 Tsay, Ruey S.,|d1951-|eauthor. 776 08 |iPrint version:|aPeña, Daniel, 1948-|tStatistical learning for big dependent data|bFirst edition.|dHoboken, NJ : Wiley, 2021.|z9781119417385|w(DLC) 2020026630 830 0 Wiley series in probability and statistics. 856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https:// learning.oreilly.com/library/view/~/9781119417385/?ar |zAvailable on O'Reilly for Public Libraries 938 Askews and Holts Library Services|bASKH|nAH37731881 994 92|bJFN