My main research area is time series analysis and forecasting. I have written a book, Time Series Analysis – Univariate and Multivariate Methods. Since the first edition was published in 1990 and the second edition in 2006, the book has been used by many researchers and universities worldwide, and I have received many encouraging letters and emails from researchers, instructors, and students about the usefulness of the book in their research and studies. It has been translated into several languages including Chinese, Spanish, and Portuguese. With the development of computers and the internet, we have had a data explosion, and many new theories and methods have been developed in high‐dimensional time series analysis. Many publishers have contacted me asking for a new edition. Because of the development of so much new material, it would be impractical to include it all in a new edition of the book. Therefore, I decided to write a new book and call it Multivariate Time Series Analysis and Applications. Due to the enthusiasm of multiple publishing editors, I had a difficult time deciding which publisher to choose. After consulting with Dr. Sarkar, our department chair, I decided to choose Wiley. After four stages of rigorous reviews, the Wiley directors unanimously approved the publication of this book.

Many research studies involve multivariate time series. For example, a study of monthly cancer rates in the United States during the past 10 years can involve 50, many hundreds, or even thousands of time series depending on whether we investigate the cancer rates for states, cities, or counties, and a study of the quarterly sales performance of one company's different branches in the country or the world from 2010 to 2018 may involve many hundreds or thousands of time series, depending on the number of products and the number of branches within the company. Multivariate time series analysis methods are needed to properly analyze the data in these studies, which are different from standard statistical theory and methods based on random samples that assume independence. Dependence is the fundamental nature of time series. The use of highly correlated high‐dimensional time series data introduces many complications and challenges. The methods and theory to solve these issues will make up the contents of this book. After introducing the fundamental concepts and reviewing the standard vector time series models, we will explore many important issues, including multivariate time series regression, dimension reduction and model simplification, multivariate GARCH (generalized autoregressive conditional heteroskedasticity) models, repeated measurement phenomenon, space–time series modeling, multivariate spectral analysis for both stationary and nonstationary vector time series, and the high‐dimension problem in multivariate time series analysis.

In this book, I follow the same fundamental themes of my research with a balanced emphasis on both theory and applications. Methodologies are introduced with proper theoretical justifications and illustrated with simulated and empirical data sets. It should be pointed out that this book is designed for a research‐oriented second time series analysis course and therefore standard exercises normally found in the first course will not be provided. Instead, research‐oriented projects will be suggested.

I am grateful to the Department of Statistical Science, Fox School of Business, and Temple University for granting me a sabbatical research leave to finish this book. Fox School is celebrating its 100‐year anniversary in 2018, and this book is my way of joining in its centennial celebration. I am also grateful to my wife, Susanna, a professor at Saint Joseph's University, sons and daughter, Stephen, Stanley, and Jessica, who are all married physicians, for their help in proofreading my manuscripts even as they are so busy with their own careers and families. My very sincere thanks go to Zeda Li, Andrew Gehman, Nandi Shinjini, and Kaijun Wang, who are either my Ph.D. dissertation students, research/teaching assistants, or both, and Kevin Liu, who was one of our excellent Masters students in the department, for their outstanding assistance in collecting data sets and developing software programs used in the book. Finally, I would like to thank Ms. Alison Oliver, Publications Manager, and Ms. Jemima Kingsly, Project Editor; Ms. Francesca McMahon, Editorial Assistant, Ms. Blesy Regulas, Project Editor; Mr. Mustaq Ahamed Noorullah, Production Editor, all at John Wiley & Sons, and Ms. Lynette Woodward, Editorial & Academic Services, UK, who have been invaluable in completing this important book project.