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, 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.

Prerequisite
Basic concepts of probability theory, working knowledge of statistical inference, linear models, and matrix algebra.

Textbook
References
Software
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 Network.

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

Assignments