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
series An automatic procedure to build univariate ARMA models for
individual components of a large data set Powerful outlier
detection procedures for large sets of related time series New
methods for finding the number of clusters of time series and
discrimination methods, including vector support machines, for time
series Broad coverage of dynamic factor models including new
representations and estimation methods for generalized dynamic
factor models Discussion on the usefulness of lasso with time
series and an evaluation of several machine learning procedure for
forecasting large sets of time series Forecasting 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.
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