Statistics 4825/5825
Applied Time Series, Fall 2009
Tuesday and Thursday 9:30-11am. CLAS 313.

Zhiyi Chi
Office: CLAS 319
Phone: (860) 486-6142
Office Hours: Wednesday, 10am-noon.

Notice

  1. Assignments
  2. Notes
  3. Textbook
  4. Course Material
  5. Prerequisite
  6. Course Grades
  7. Policies
  8. Exam Schedule

Assignments

Notes

Textbook
Robert H. Shumway, David S. Stoffer, Time Series Analysis and Its Application, With R Examples, 3rd Edition, Springer,
The second author has set up very helpful websites. For some of the R commands used in the book, click here. For the data set, click here.
R resources: R for Beginners. An Introduction to R. A lot more can be found at the official web site for R.

Course Material

  1. Regression with autocorrelated errors: review of simple and multiple linear regression; regression with serially correlated errors; Durbin-Watson Statistic
  2. Determinsitic time series regression and smoothing methods: structural decomposition; trend fitting by polynomial trend models; trend fitting by moving averages; seasonality fitting using seasonal indicators; seasonality fitting using trigonometric functions; steps in structural time series modeling
  3. Stochastic properties of time series: strict and weak stationarity; general linear processes and properties; estimation of mean, ACVF and ACF
  4. ARIMA models: AR, MA, ARMA, ARIMA models and properties; model identification; model estimation; further look at residuals for structure; model adequacy and model selection; prediction
  5. Regression with ARIMA Errors: simultaneous fitting of regression and time series model parameters; estimation; prediction
  6. Conditionally heteroscedastic time series: ARCH/GARCH model fitting
  7. State space models: dynamic linear models, Kalman filter and smoother

Prerequisite
Stat 3025Q and/or consent of instructor.

Course Grades
Your final grade will be based on total 100 points performance. The letter grades A, B, C, D will approximately correspond to 90%, 80%, 70%, 60%, respectively. A total grade under 50% will definitely constitute an F.

ItemTotal points
Homework 30%
Midterm Exam30%
Final Exam40%

Policies
Every student enrolled in this course must follow the Student Code. Following are policies specific to the course.

Homeworks

  • Collaboration is not allowed for homework under any circumstance.
  • Data analysis in homework problems must be done with R regardless of what is specified in the problems.
  • No late homeworks will be accepted

    • Exams

  • The exams are open-book and open-notes.
  • Make sure to bring calculators in order to solve data analysis problems in the exams.
  • Except for medical emergencies with appropriate documents from doctor, no make-up exams will be given;
  • If you have a conflict that will not allow you to take the exam as scheduled, you must notify your instructor at least two weeks in advance; and if you need to reschedule, you will have to take the exam before the scheduled date.

    • For both homeworks and exams
    Presentation is important in order for your solutions to receive full credit. Points will be deducted for messy and illegible answers. Make sure to staple all the pages together and in order — the grader can only grade pages he/she has! Make sure you staple all the pages together and in order and print your name legibly on the first page.

    Exam Schedule

     DateTimeLocation
    Midterm Wed, Oct 22 In class CLAS 313
    Final Thu, Dec 17 8-10am CLAS 313

    Note: The midterm exam will be held during one regular class meeting, in CLAS 313. The final exam will be held according to Registrar Office's schedule.

    Both exams are open-book and open-notes. You can use a calculator. Presentation is important in order for your solutions to receive full credit. Points will be deducted for messy and illegible answers. Make sure to staple in order all the pages together — the grader can only grade pages he/she has! Make sure to print your name legibly on the first page.