Instructor

• phone number: 543-3866 (Department of Statistics office)
• e-mail address: dbp@uw.edu
• office hours: Thursdays from 12:30PM to 3:30PM in C-310 of Padelford Hall (quite close to the Math Library)

Prerequisites

Textbooks

• Introduction to Time Series and Forecasting (Third Edition) by Peter J. Brockwell and Richard A. Davis, Springer, 2016;
• Time Series Analysis: With Applications in R (Second Edition) by Jonathan D. Cryer and Kung-Sik Chan, Springer, 2008; and
• Time Series Analysis and Its Applications: With R Examples (Fourth Edition) by Robert H. Shumway and David S. Stoffer, Springer, 2017.

Here is some books that should be good supplements to the three recommended textbooks.

1. Brockwell, P.J. and Davis, R.A. (1991), Time Series: Theory and Methods (Second Edition), New York: Springer. This earlier book by the same authors of Introduction to Time Series and Forecasting goes into time series analysis at a deeper level than their later book or the books by Cryer & Chan and Shumway & Stoffer.
2. Chatfield, C. (2004), The Analysis of Time Series: An Introduction (Sixth Edition), Boca Raton: Chapman & Hall. This book assumes less statistical background than the three recommended textbooks and does not go into as much mathematical detail, but is nonetheless quite useful.
3. Cowpertwait, P.S.P. and Metcalfe, A.V. (2009), Introductory Time Series with R, New York: Springer. I don't know much about this book, other than that it also makes use of R and is available on-line for the UW community.
4. Diggle, P.J. (1990), Time Series: A Biostatistical Introduction, Oxford: Oxford University Press. About the same level as Cryer & Chan, with some examples focusing on biostatistical applications.
5. Palma, W. (2016), Time Series Analysis, Hoboken, New Jersey: John Wiley & Sons. About the same level as Brockwell & Davis and Shumway & Stoffer, with some nice extensions into material not covered by those two books (uses R for computations).
6. Shumway, R.H. and Stoffer, D.S. (2019), Time Series: A Data Analysis Approach Using R, Boca Raton: Chapman & Hall. While the recommended Shumway & Stoffer textbook for Stat 519 is a graduate-level text, the intended audience for this book by the same authors is undergraduate students and non-majors.
7. Woodward, W.A., Gray, H.L. and Elliott, A.C. (2017), Applied Time Series Analysis with R (Second Edition), Boca Raton, Florida: CRC Press. A nicely written book on about the same level as Brockwell & Davis and Shumway & Stoffer, with some nice extensions into material not covered by those two books (uses R for computations)..
8. Zivot, E. and Wang, J. (2006), Modeling Financial Time Series with S-Plus (Second Edition), New York: Springer. The first author is a professor in the UW Department of Economics, and the book is available on-line for the UW community. As its title indicates, this book emphasizes economic and financial time series and should be consulted by students interested in these areas.

There will be seven homework assignments. I will post a problem set on Fridays accessible via the Assignments link on this Web site (the first set will be posted on 10 January, and the seventh (and final) set, on 21 February). The homework will be collected at the beginning of the Friday class the week after the problem set is posted. Late homework will not be accepted. Electronic submissions will not be accepted (you need to turn in a hardcopy of your solutions to me in class).

Students in the past who have gotten the most out of this course have worked on the homework assignments by themselves. I thus discourage group efforts, but feel free to discuss homework problems with your classmates as long as the solutions you turn in reflect your own work and not a group effort (copying is certainly not permitted either from each other or from solutions to certain problems that can be tracked down from the Internet or elsewhere). While grading of your homework will be based primarily on technical correctness, lack of clarity and sloppiness can affect your grade. If I have to spend extra time trying to figure out exactly what your solution is or if it is not clear what steps you took in getting to your answer, I will deduct points - take this as fair warning to be as clear as possible! Also I frown upon excessive reliance on Mathematica (or software of a similar ilk). If a homework problem says `assuming X to be true, show that Y follows,' I do not consider `I took X, put it into Mathematica, and Y came out' to be an acceptable answer.

There will be a 50 minute exam on Friday, 6 March, which will count for 20% of your course grade. The exam will be closed book, and use of any sort of electronic device during the exam is not allowed; however, you will be allowed to bring in a single sheet of standard 8.5 by 11 inch paper on which you can write anything you so desire (you may use both sides of the paper).

The term project is an essential and important part of this course. It could be a data analysis, a simulation study, methodological or theoretical research, or a report on a research article of interest to you. Topics for the project must be approved by me no later than Friday, 21 February. You are expected to provide a concise written report (approximately 5 pages to a maximum of 10 pages in length; double-spaced single-column pages using a font size that won't strain my eyes - a 9 point font is recommended). The report will be due by 2PM on Friday, 20 March (the last day of the quarter). I will need both a paper copy (you can hand it to me in my office (C-310, Padelford) that Friday from 12:30PM to 2PM) and a PDF file (to be e-mailed to me (dbp@uw.edu) by Friday evening). You should plan on giving a short presentation about your term project (5 to 10 minutes in length, depending on class size - the exact length will be announced in late January). The presentations will be given during either one of final two class periods (Wednesday, 11 March, and Friday, 13 March, 12:30PM to 1:20PM) or the time slot allocated for a final exam for the class (Thursday, 19 March, starting at 8:30AM). For more information, see the Term Project link on this Web site.

Software for Time Series Analysis

Topics to be Covered

• introduction to time series analysis
• simple time series models and stationary processes
• simple methods for handling trend and seasonality
• basic theory for ARMA processes
• modeling and forecasting time series with ARMA processes
• nonstationary models with stationary differences
• seasonal time series models
• modeling long-range dependence

This list of topics is tentative and subject to revision. Time permitting, I would like to touch on some other important topics in time-domain analysis: regression models with time series errors, state-space models, Kalman filtering, nonlinear models, ARCH/GARCH models and multivariate time series. (All three textbooks have chapters devoted to spectral analysis (also called frequency-domain analysis), but we will be focusing entirely on time-domain analysis. I'll be happy to answer any questions you have about spectral analysis during office hours.)