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 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.

這本書《大型相依資料的統計學習》提供了對於分析和預測大型且動態相依資料集的統計學和機器學習方法的全面介紹。書中介紹了用於建模和預測大量時間序列資料的自動程序。從可視化工具開始，書中討論了在大型相依資料中尋找異常值、群集和其他異質性的程序和方法。接著介紹了各種降維方法，包括在動態相依和動態因子模型中的正則化和正則化Lasso。書中還涵蓋了其他預測程序，包括指數模型、偏最小二乘法、提升和即時預測。此外，書中還介紹了機器學習方法，包括神經網絡、深度學習、分類和回歸樹以及隨機森林。最後，還介紹了建模和預測時空相依資料的程序。

在整本書中，對所討論方法的優點和缺點進行了詳細說明。書中使用真實世界的例子來演示應用，包括使用多個R套件。最後，書中還提供了一個與書籍相關的R套件，以幫助讀者重現例子中的分析並促進實際應用。

《大型相依資料的分析》涵蓋了多個建模和理解大型相依資料的主題，包括：

- 繪製大量時間序列的新方法
- 自動建立大型資料集中各個組成部分的單變量ARMA模型的程序
- 用於大量相關時間序列的強大異常值檢測程序
- 尋找時間序列群集數量和區分方法的新方法，包括時間序列的向量支持機
- 廣泛涵蓋動態因子模型，包括廣義動態因子模型的新表示和估計方法
- 討論Lasso在時間序列中的實用性以及評估多種機器學習程序用於預測大量時間序列
- 使用外生變數預測大量時間序列，包括指數模型、偏最小二乘法和提升的討論
- 引入現代程序用於建模和預測時空資料

這本書非常適合商業、經濟、工程和科學領域的博士生和研究人員閱讀，同時也適合這些領域的從業人員，希望提高他們對於分析和預測大型相依資料的統計學和機器學習方法的理解。

Daniel Peña, 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.

Daniel Peña, PhD 是西班牙馬德里卡洛斯三世大學統計學教授。他於1976年從馬德里理工大學獲得博士學位，並曾在威斯康辛大學麥迪遜分校、芝加哥大學和卡洛斯三世大學任教。他在卡洛斯三世大學擔任校長職務的時間為2007年至2015年。

Ruey S. Tsay, PhD 是芝加哥大學布斯商學院的H.G.B Alexander計量經濟學和統計學教授。他於1982年從威斯康辛大學麥迪遜分校獲得博士學位。他的研究專注於商業和經濟預測、金融計量學、風險管理和大數據分析領域。