Catalog Description: Linear time series models, moving average (MA), autoregressive (AR), ARMA and ARIMA models, estimation and forecasting, interval predictions, forecast errors, stationary processes in the frequency domain, state-space models. This course is offered as both AMS 316 and AMS 586.
Prerequisite: AMS 311 and AMS 315.
AMS 315 and 316 satisfy the Validation by Educational Experience program. For more details about actuarial preparation at Stony Brook see Actuarial Program and the Society of Actuaries.
Text: Analysis of Time Series, 6th Edition by Chatfield and Taylor
ISBN# 9781584883173
THIS COURSE IS OFFERED IN THE FALL SEMESTER ONLY.
Week 1. |
Introduction and examples |
Week 2. |
Simple descriptive techniques, trend, seasonality, the correlogram |
Week 3. |
Linear time series models and examples |
Week 4. |
moving average (MA), autoregressive (AR) and examples |
Week 5. |
ARMA model and examples |
Week 6. |
ARIMA model and examples |
Week 7. |
Data analysis with time series models |
Week 8. |
Estimation and examples |
Week 9. |
Model identification and fitting |
Week 10. |
Interval predictions and examples |
Week 11. |
Forecasting, forecast errors and examples |
Week 12. |
Stationary processes in the frequency domain: The spectral density function, the periodogram, spectral analysis. |
Week 13. |
State-space models: Dynamic linear models and the Kalman filter |