Applied Time Series Analysis

Syllabus

• course description, exam dates, etc (in pdf format).

Home Work

Make-up Class

Text-book Webpage

• Useful info on the text-book "Time Series Analysis with Applications in R, by Jonathan D. Cryer and Kung-Sik Chan

Sources of interesting time series data

• unequally spaced under-ground water level data.
• A very comprehensive time series library.

Old Computer demonstrations from Spring 2009

• R codes run in the class on Jan. 21.

Old Computer demonstrations from Spring 2009

• R codes run in the class on Jan. 21.
• R codes run in the class on Jan. 23.
• R codes run in the class on Jan. 26.
• R codes run in the class on Jan. 28.
• R codes run in the class on Feb 6.
• R codes run in the class on Feb 9.
• R codes run in the class on Feb 11.
• R codes run in the class on Feb 13.
• R codes run in the class on Feb 16.
• R codes run in the class on Feb 23.
• R codes run in the class on Feb 25.
• R codes run in the class on Feb 27.
• R codes run in the class on March 6.
• R codes run in the class on March 9.
• R codes run in the class on March 11.
• R codes run in the class on March 13.
• R codes run in the class on March 23.
• R codes run in the class on March 25.
• R codes run in the class on March 27.
• R codes run in the class on March 30.
• R codes run in the class on April 1.
• R codes run in the class on April 15.
• R codes run in the class on April 20.
• R codes run in the class on April 22.
• R codes run in the class on April 24.
• R codes run in the class on April 29.
• R codes run in the class on May 4.
• R codes run in the class on May 6.
• R codes run in the class on May 8.

Software

• Statistical softwares: We shall have to use R for statistical analysis.
• R is available at the ITC in Schaeffer Hall.
• R can be freely down-loaded from R Project homepage
• Here is a version of R on the web.

  How to prepare for the project report

  • The report should contain:
  • 1. A title, your name and the course title
  • 2. A summary of the main findings that should not exceed 200 words; it should contain the final model and important conclusions from the analysis.
  • 3. The report then contains a description of the dataset, some of its scientific background and the interesting features of the data (time plot, histogram, stationarity, needs for differencing?). The model specification, estimation and model diagnostics (residual plot, acf, pacf of the residuals, Box-Ljung statistic, outliers?). Report of the final model with justification and interpretation of the fitted model (significance of coefficients including the intercept term.)
  • 4. The report should either provide an internet address where the dataset can be downloaded, or the data included in an attached computer diskette.
  • Points will be deducted if these guidelines are not followed.

  Old Exams

  • Exam 1 given in Spring, 08
  • Exam 2 given in Spring, 08
  • Exam 1 given in Spring, 03
  • Exam 1 given in Spring, 95.
  • Exam 2 given in Spring, 95.
  • Exam 2 given in Spring, 02.
  • Final exam given in Spring, 94. (Part of last question is missing.)
  • Final exam given in Spring, 01.
  • Exam 1 given in Spring, 02
  • Final exam given in Spring, 02.

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