STAT 5379 -- Time Series Analysis -- Spring 2025
Instructor
Dr. Alex
Trindade, 233 Mathematics & Statistics Building.
E-mail: alex.trindade"at"ttu.edu.
Course Meets: TR 14:00 - 15:50 in MATH 109.
Office Hours: TWR 1:00-2:00, or by appointment.
Text Books
Required:
- Introduction to Time Series and Forecasting, Brockwell and Davis, 3rd edition (2016), Springer. [Denoted by B&D.]
- Time Series Analysis and its Applications: With R Examples,
Shumway & Stoffer, 4th edition (2017), Springer. [Denoted by S&S.]
Helpful:
- Time Series Analysis, Wilfredo
Palma, (2016), Wiley. (Similar coverage.)
- Applied Time Series Analysis, Woodward, Gray & Elliott, 2nd edition (2017), CRC Press. (Similar coverage.)
- Time Series Analysis: With Applications in R , Cryer & Chan, 2nd edition (2008), Springer. (Similar coverage.)
- Forecasting: principles and practice, Hyndman & Athanasopoulos (2013), OTexts. (Updated coverage of forecasting methods.)
- Time Series: Theory and Methods, Brockwell and
Davis, 2nd edition (1991), Springer. (More rigorous than their "Intro" book.)
Course Notes
I intend to closely adhere to these notes which were transcribed by a student in the spring 2019 class. We will refer often to these particular plots.
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):
- Chapt 1: Characteristics of Time Series.
- Chapt 2: Time Series Regression and Exploratory Data Analysis.
- Chapt 3: ARIMA Models.
- Chapt 4: Spectral Analysis.
- Chapt 5: Specialized Topics: long-memory, unit-roots, lagged regression and nonlinear models.
- Chapt 6: State-Space Models.
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:
- A: 90-100%.
- B: 80-89%.
- C: 70-79%.
- D: 60-69%.
- F: 0-59%.
The test schedule is as follows:
- Test 1: Feb 27 (after Hwk 5).
- Test 2: Apr 10 (after Hwk 9).
- Final Exam: TBA.
Homework Problem Sets
There will be weekly problem sets which will be assigned from the following files:
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.
- Set 0 (due Jan 18): Assignment 1: Problem 1 (not graded).
- The remaining Sets are on Blackboard (Hwk 1 is due Fri Jan 24.)
Software
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".
Policies
- Required Texas Tech Policies can be found here.
- Recommended Texas Tech Policies can be found here.
- Use of Generative AI Tools. The use of generative AI tools (such as ChatGPT) is not permitted in this course; therefore, any use of AI tools for work in this class may be
considered a violation of Texas Tech's Academic Integrity policy and
the Student Code of Conduct since the work is not your own. The use of
unauthorized AI tools will result in referral to the Office of Student
Conduct.
- Electronic Devices in Tests. In the spirit of keeping costs down, I will permit the usage of apps on smart devices (phones, tablets, laptops, etc.), but any kind of communication or accessing of the web via these devices is forbidden.
- Collaboration. My policies on this are as follows.
- Homeworks: Discussion with peers regarding material/concepts covered in the
course is permitted, and is encouraged since it usually leads to greater comprehension. However, each person must write up his/her own
solution to a particular problem, and not simply have someone else do it for them.
- Tests: Any form of collaboration on tests, including e-device communication or trying to see what the person next to you is writing, is strictly forbidden and will not be tolerated.
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