Statistical Learning for Big Dependent Data (Wiley Series in Probability and Statistics) 1st Edition Daniel Peña

Statistical Learning for Big Dependent Data (Wiley Series in Probability and Statistics) 1st Edition Daniel Peña – Ebook Instant Download/Delivery ISBN(s): 9781119417385,1119417384

Product details:

• ISBN 13:9781119417415
• Author:Daniel Peña, Ruey S. Tsay

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.

Table contents:

1. Introduction To Big Dependent 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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