Statistics 520

Time Series and Applications

Spring 2021

Office Hour on Friday 4/2/21 will start at 12:10pm.

The midterms will be on Wednesdays, 3/3 and 4/21, at 8-10pm in LWSN B151; you can bring 4 pages of crib sheets, letter-size double-sided. Old exams will be posted at appropriate times.

An old exam can be found here, showing you the format of the exam.

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

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

References

• Time Series: Theory and Methods, by Brockwell and Davis.
• Spectral Analysis and Time Series, by Priestley.
• An Introduction to R, by the R Development Core Team.
• Using R for Data Analysis and Graphics - An Introduction, by Maindonald.
• Modern Applied Statistics with S-Plus, by Venables & Ripley.

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; the assignments are due at Brightspace under assignments. There will also be two in-person written midterms and a final lab project. The midterms are closed-book but you are allowed 4 pages of letter-size double-sided crib sheets.

Grading

The letter grade will be based on assignments (30%), midterms (2 x 30%), and the final project (10%).

Lecture Notes

Assignments