Statistical Learning for Big Dependent Data

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 analysing 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 visualisation tools, the book discusses procedures and methods for identifying outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularisation and factor models such as regularised 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. Furthermore, it presents machine-learning methods, including neural networks, deep learning, classification and regression trees, and random forests.

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. Real-world examples to demonstrate applications are used, including the use of many R packages. 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 modelling and understanding big dependent data, such as:

• 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 generalised dynamic factor models
• Discussion on the usefulness of Lasso with time series and an evaluation of several machine learning procedures 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 modelling 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 on the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analysing and forecasting big dependent data.