Time Series

Lectures TR 1000-1120 GLSN 100

Instructor: Charlotte Wickham, 255 Weniger
charlotte.wickham@stat.oregonstate.edu

Instructor Office hours: Monday & Wednesday 1-2pm, Weniger 255

TA: Chris Comiskey comiskec@onid.oregonstate.edu

TA Office hours: Tuesday & Friday 1-2pm Kidder 250

Week 1

Introduction, decomposition and graphics

• Jan 5. Welcome
  • Code to generate examples
• Jan 7. EDA tools
  • Code for in class

Week 2

Trend, decomposition and residual correlation

Reading: Chatfield Chapter 2

• Jan 12. Decomposition and trend
  • R code
• Jan 14. Variance and residuals
  • R code

Week 3

Basic time series models

Reading: Chatfield Chapter 3

• Jan 19. Basic models
  • Doc cam notes
• Jan 21. ARMA models
  • Doc cam notes

Week 4

Fitting ARMA models

Reading: Chatfield Chapter 4

• Jan 26. Estimation
  • Doc cam notes
• Jan 28. Fitting ARMA models

Week 5

Forecasting ARMA models

Reading: Chatfield Chapter 5

• Feb 2. (S)ARIMA and forecasting
  • Oil price example
  • CO2 example
  • Doc cam notes
• Feb 4. Forecasting
  • Temperature example
  • Doc cam notes

Week 6

Reading: Shumway & Stoffer 5.5, Alternative to read

• Feb 9. Regression with correlated errors
  • R code
• Feb 11. In class midterm

Week 7

Regression & Spectrum

Reading: Shumway & Stoffer 5.5, Alternative to read, Also Chatfield 6

• Feb 16. Spurious regression
  • Simulation code
  • Paired DA code
  • My simulation code
  • My Bluebird analysis
  • My boardings analysis
• Feb 18. Spectrum

Week 8

Reading: Chatfield 7

• Feb 23. Periodogram
  • R examples
• Feb 25. Harmonic Regression
  • lectures/15-harmonic-reg.R

Week 9

Reading: Chatfield 8

• Mar 4. Bivariate series and mortality revisited
  • Cross covariance code
  • Mortality example
• Mar 6. Project work day

Week 10

Wrapping up

• Mar 8. Other topics
• Mar 10. Project presentations & Review