Statistics 520
Time Series and Applications
Spring 2017
Instructor: Chong Gu
Classes: 12:30  1:20 MWF, REC 114
Office Hours: 2:30  3:30 MWF, HAAS 170, or by appointment
The final will be on Tuesday, May 2, 13pm, in REC 114. You can bring 4 lettersize, doublesided crib sheets, and a calculator; no mobile devices. Here is an old final.
 Course outline
 This course offers an introduction to the analysis of time
series. Topics to be covered include the autocorrelation and
spectrum of stationary processes, the structure, estimation, and
identification of AutoRegressive (Iterated) Moving Average
(ARIMA) models, forecasting, model diagnostics, seasonal models,
and transfer function models. Software tools will be an
important part of the course, for which we will mainly draw on
the resources available in R, an opensource programming
environment for data analysis and graphics.
 Prerequisite
 Basic concepts of probability theory, working knowledge of
statistical inference, linear models, and matrix algebra.
 Textbook

 Time Series Analysis: Forecasting and Control, by Box, Jenkins, and Reinsel.
The data sets used in the text are given here.
 References

 Software
 We will be using R, an opensource programming environment for
data analysis and graphics, as the primary platform for
computation and graphics. R resources are to be found at The Comprehensive R Archive
Network.
 Course Work
 There will be about 78 assignments, with "written" and "lab"
problems mixed in. There will also be a midterm and a final. The
assignments will contribute about 40% to the course grade and the
exams 30% each.
 Lecture Notes
 Assignments

