Time Series and Applications
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, 1-3pm, in REC 114. You can bring 4 letter-size, double-sided 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 open-source programming
environment for data analysis and graphics.
- Basic concepts of probability theory, working knowledge of
statistical inference, linear models, and matrix algebra.
- Time Series Analysis: Forecasting and Control, by Box, Jenkins, and Reinsel.
The data sets used in the text are given here.
- We will be using R, an open-source 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
- Course Work
- There will be about 7--8 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