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.

About the Author

Daniel Pe�a, PhD, is Professor of Statistics at Universidad Carlos III de Madrid, Spain. He received his PhD from Universidad Politecnica de Madrid in 1976 and has taught at the Universities of Wisconsin-Madison, Chicago and Carlos III de Madrid, where he was Rector from 2007 to 2015.

Ruey S. Tsay, PhD, is the H.G.B Alexander Professor of Econometrics & Statistics at the Booth School of Business, University of Chicago, United States. He received his PhD in 1982 from the University of Wisconsin-Madison. His research focuses on areas of business and economic forecasting, financial econometrics, risk management, and analysis of big dependent data.

"About this title" may belong to another edition of this title.

Store Description

Visit Seller's Storefront

Seller's business information

Websew.com Inc
22C Cragwood Road, Avenel, NJ, 07001, U.S.A.

Sale & Shipping Terms

Terms of Sale

We sell brand new books from the publisher.

Right of withdrawal

If you are a consumer you can withdraw from the contract in accordance with the following. Consumer means any natural person who is acting for purposes which are outside his trade, business, craft or profession.

Information regarding the right of withdrawal

Statutory right to withdraw

You have the right to withdraw from this contract within 14 days without giving any reason.

The withdrawal period will expire after 14 days from the day on which you acquire, or a third party other than the carrier and indicated by you acquires, physical possession of the last good or the last lot or piece.

To exercise the right of withdrawal, electronically fill in and submit a clear statement on our website, under "My Purchases" in "My Account". We will communicate to you an acknowledgement of receipt of such a withdrawal on a durable medium (e.g. by e-mail) without delay.

To meet the withdrawal deadline, it is sufficient for you to send your communication concerning your exercise of the right of withdrawal before the withdrawal period has expired.

Effects of withdrawal

If you withdraw from this contract, we will reimburse to you all payments received from you, including the costs of delivery (except for the supplementary costs arising if you chose a type of delivery other than the least expensive type of standard delivery offered by us).

We may make a deduction from the reimbursement for loss in value of any goods supplied, if the loss is the result of unnecessary handling by you.

We will make the reimbursement without undue delay, and not later than 14 days after the day on which we are informed about your decision to withdraw from this contract.

We will make the reimbursement using the same means of payment as you used for the initial transaction, unless you have expressly agreed otherwise; in any event, you will not incur any fees as a result of such reimbursement.

We may withhold reimbursement until we have received the goods back, or you have supplied evidence of having sent back the goods, whichever is the earliest.

You shall send back the goods or hand them over to INDOO, Avenel, New Jersey, U.S.A., without undue delay and in any event not later than 14 days from the day on which you communicate your withdrawal from this contract to us. The deadline is met if you send back the goods before the period of 14 days has expired. You will have to bear the direct cost of returning the goods. You are only liable for any diminished value of the goods resulting from the handling other than what is necessary to establish the nature, characteristics and functioning of the goods.

Exceptions to the right of withdrawal

The right of withdrawal does not apply to:

The delivery of newspapers, journals or magazines with the exception of subscription contracts; and
The supply of digital content which is not supplied on a tangible medium (e.g. on a CD or DVD) if you accepted when you placed your order that we could start to deliver it, and that you could not withdraw once delivery had started.

Shipping Terms

Shipping costs are based on books weighing 2.2 LB, or 1 KG. If your book order is heavy or oversized, we may contact you to let you know extra shipping is required.

Shipping rates within U.S.A.

Shipping rates within U.S.A.
Order quantity	5 to 14 business days	5 to 14 business days
First item	� 0.00	� 0.00

Delivery times are set by sellers and vary by carrier and location. Orders passing through Customs may face delays and buyers are responsible for any associated duties or fees. Sellers may contact you regarding additional charges to cover any increased costs to ship your items.