Wayne A. Woodward - Time Series for Data Science: Analysis and Forecasting (Chapman & Hall/CRC Texts in Statistical Science)

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Time Series for Data Science

Analysis and Forecasting

Wayne A. Woodward, Bivin Philip Sadler and Stephen Robertson

For more information about this series, please visit: https://www.routledge.com/Chapman--HallCRC-Texts-in-Statistical-Science/book-series/CHTEXSTASCI

Wayne A. Woodward, Bivin P. Sadler and Stephen D. Robertson

First edition published 2022

by CRC Press

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and by CRC Press

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CRC Press is an imprint of Taylor & Francis Group, LLC

2023 Wayne A. Woodward, Bivin Philip Sadler and Stephen Robertson

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Library of Congress CataloginginPublication Data

Names: Woodward, Wayne A., author. | Sadler, Bivin Philip, author. | Robertson, Stephen (Lecturer in statistics), author.

Title: Practical time series analysis for data science / Wayne A. Woodward, Bivin Philip Sadler and Stephen Robertson.

Description: First edition. | Boca Raton : CRC Press, 2022. | Series: Statistics | Includes bibliographical references and index.

Identifiers: LCCN 2021048203 (print) | LCCN 2021048204 (ebook) | ISBN 9780367537944 (hardback) | ISBN 9780367543891 (paperback) | ISBN 9781003089070 (ebook)

Subjects: LCSH: Time-series analysis. | Autoregression (Statistics) | Big data.

Classification: LCC QA280 .W686 2022 (print) | LCC QA280 (ebook) | DDC 519.5/5dc23/eng/20211223

LC record available at https://lccn.loc.gov/2021048203

LC ebook record available at https://lccn.loc.gov/2021048204

ISBN: 978-0-367-53794-4 (hbk)

ISBN: 978-0-367-54389-1 (pbk)

ISBN: 978-1-003-08907-0 (ebk)

DOI: 10.1201/9781003089070

Typeset in Times

by Newgen Publishing UK

To Beverly, Ellie, and Melissa

and in memory of

Henry L. (Buddy) Gray

We believe this to be a truly unique textbook for teaching introductory time series and feel it is appropriate for masters level and undergraduate students in data science, statistics, mathematics, economics, finance, MBA programs, and any of the sciences. The contents of this book were developed from time series courses taught by the authors in two applied masters programs at Southern Methodist University: an Applied Statistics/Data Analytics program within the Statistical Science Department and an online Master of Science in Data Science.

The goal of this text is to provide students with an understandable and friendly introduction to time series analysis that will provide them with a fundamental understanding of time series analysis and will equip them with a wide range of tools they can use in the analysis of time series data in their careers. Our experience is that we have been successful in accomplishing these goals. We have learned how to effectively address this audience. Many students have stepped out of this course and begun using the tools in this book to analyze time series data on the job. We have received encouraging feedback from students, professors, and practitioners, alike. Were excited about the value you will find as well!

This book covers a variety of time seriesrelated topics. We devote the entirety of presents a clear coverage of increasingly popular neural network/deep learning methods for analyzing time series data.

Prerequisites for the readers of the text are relatively minimal. A calculus background is valuable but not necessary for students using this book. The book is surprisingly advanced without falling back on calculus-based derivations. We believe that the book is accessible to serious students who have not had courses in calculus or a statistics course beyond the introductory level. While we are concerned that this may turn off some instructors, we believe that you will be surprised at the mathematical rigor yet applied nature of this book. Give it a try! For instructors or students interested in more mathematical detail, we have provided supplemental videos produced by the authors, in-depth appendices, and references to related resources (textbooks, journal articles, etc.).

Of course, to truly master these tools, the student must apply the methods and practice, practice, practice. The book contains problem sets using real-world datasets and interesting and relevant questions. In addition, we carefully designed chapter problems to specifically address learning outcomes associated with the chapter. The result is a set of problems that can reasonably be completed in full by a student to assist in efficiently facilitating mastery of the concepts in each chapter. We feel this allows for students, upon completion of their study, to confidently be able to say to themselves and others that they fully grasp the concepts covered in this book.