Ulteriori informazioni
This textbook is designed for an introductory time series course where the prerequisites are an understanding of linear regression and some basic probability skills. All of the numerical examples were done using the R statistical package, and the code is typically listed at the end of an example.
Sommario
1. Time Series Characteristics.
2. Time Series Regression and EDA.
3. ARIMA Models.
4. Spectral Analysis and Filtering.
5. Some Additional Topics.
Info autore
Robert H. Shumway is Professor Emeritus of Statistics, University of California, Davis. He is a Fellow of the American Statistical Association and has won the American Statistical Association Award for Outstanding Statistical Application. He is the author of numerous texts and served on editorial boards such as the
Journal of Forecasting and the
Journal of the American Statistical Association.
David S. Stoffer is Professor of Statistics, University of Pittsburgh. He is a Fellow of the American Statistical Association and has won the American Statistical Association Award for Outstanding Statistical Application. He is currently on the editorial boards of the Journal of
Forecasting, the
Annals of Statistical Mathematics, and the
Journal of Time Series Analysis. He served as a Program Director in the Division of Mathematical Sciences at the National Science Foundation and as an Associate Editor for the
Journal of the American Statistical Association and the
Journal of Business & Economic Statistics.
Riassunto
This textbook is designed for an introductory time series course where the prerequisites are an understanding of linear regression and some basic probability skills. All of the numerical examples were done using the R statistical package, and the code is typically listed at the end of an example.
Testo aggiuntivo
"The intended audience of the book are mathematics undergraduates taking a one semester course on time series. . . The authors frame learning time series primarily by extending concepts from linear models. Personally, I favour this approach, since it allows the book to clearly signpost similarities and differences between concepts in both topics and provides a natural learning progression from what most undergraduate students will already be familiar with . . .This book successfully delivers a practical tool-based approach to time series analysis at an introductory level, complementing the existing texts from the authors, which are aimed at a more advanced audience."~Matthew Nunes, Journal Times Series Analysis
