Statistical learning for big dependent data / Daniel Peña, Ruey S. Tsay.

Format:

Book

Author/Creator:

Peña, Daniel, 1948- author.

Tsay, Ruey S., 1951- author.

Series:

Wiley series in probability and statistics.

Wiley series in probability and statistics

Language:

English

Subjects (All):

Big data--Mathematics.

Big data.

Time-series analysis.

Data mining--Statistical methods.

Data mining.

Forecasting--Statistical methods.

Forecasting.

Physical Description:

1 online resource (563 pages).

Edition:

First edition.

Place of Publication:

Hoboken, New Jersey : Wiley, [2021]

Summary:

"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"-- Provided by publisher.

Contents:

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.

Notes:

Description based on print version record.

ISBN:

9781119417415

1119417414

9781119417408

1119417406

9781119417392

1119417392

OCLC:

1193558110