STAT 5379 -- Time Series Analysis -- Spring 2021


Dr. Alex Trindade, 228 Mathematics & Statistics Building.
E-mail: alex.trindade"at"
Course Meets: TR 9:30 - 10:50 in Media & Communication 155. When needed it will also meet on this zoom link (Passcode: 060443).
Office Hours: TWR 2:00-3:00, via this zoom link.

Text Books

Required: Helpful:

Course Notes

I intend to closely adhere to these notes which were transcribed by a student in the spring 2019 class.

Course Objectives and Syllabus

The course is aimed at providing a solid introduction to the methods and underlying theory of modern time series analysis. The main focus will be on modeling and forecasting second order stationary processes with ARMA models. The latter part of the course aims to cover more advanced/specialized topics like spectral analysis, state-space models, and nonlinear models. Applications will involve the use of R. The prerequisite for the course is the graduate level mathematical statistics sequence STAT 5328-5329. Chapters to be covered are as follows (S&S):

Expected Student Learning Outcomes

The heart of the course will consist of chapters 1-3 of the text, concentrating on the following major topics: Stationary processes; autocorrelation; ARIMA models; model-based inference; prediction. By the end of the course the student should have a good grasp of concepts, theory and methodology pertaining to the analysis of time series data: accounting for trend & seasonality; assessing the presence of autocorrelation; ARIMA model fitting; regression models with ARMA noise; model-based inference & prediction.

Methods of Assessing the Expected Learning Outcomes

The expected learning outcomes for the course will be assessed through a mix of homework assignments and tests. The course grade will be determined from homework problem sets (20%), two midterm tests (25% each), and a comprehensive final exam (30%). The traditional grading scale will be used: The test schedule is as follows:

Homework Problem Sets

There will be weekly problem sets from the text due on thursdays. All work is to be uploaded to Blackboard. No late submissions will be accepted. Only a subset of the hwk may be graded; if your hwk omits the problem(s) chosen to be graded your grade will be zero. Start each problem on a new page.


We will use R as the primary software tool. Some problems and assignments will require extensive use of computing. While we will focus on the theory, the applied computing aspect is an important complement that greatly helps in understanding the methodology. For details on R see my statistical computing page, and especially the section on "time series".



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