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
2. LINEAR UNIVARIATE TIME SERIES
3. ANALYSIS OF MULTIVARIATE TIME SERIES
4. HANDLING HETEROGENEITY IN MANY TIME SERIES
5. CLUSTERING AND CLASSIFICATION OF TIME SERIES
6. DYNAMIC FACTOR MODELS
7. FORECASTING WITH BIG DEPENDENT DATA
8. MACHINE LEARNING OF BIG DEPENDENT DATA
9. SPATIO-TEMPORAL DEPENDENT DATA
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