Syllabus

Course Description:

An introduction to time series analysis in the time domain and spectral domain. Topics will include: stationarity, autoregressive moving average models, forecasting, estimation, ARIMA models, ARCH models, power spectra, nonparametric spectral estimation, parametric spectral estimation, state-space models, filtering and smoothing, and wavelets.

Prerequisites:

Stat 101, Stat 134 or consent of instructor.

Textbook:

Shumway, R. H. & Stoffer, D. S. (2000). Time Series Analysis and its Applications. New York: Springer-Verlag.

Course Format:

Three hours of lecture (MWF 3-4, 75 Evans Hall) and two hours of laboratory per week.

Homework

There will be weekly homework assignments, due one week after being passed out. Late homeworks will not be accepted.
You should try to solve problems on your own. If you discuss problems with another student, indicate on your writeup the name of your collaborator(s). In any case you must write up your own solutions.

Grading

There will be a midterm exam and a final exam. The midterm will count 20% of your grade, and the final 30%. Homework will count 25% and labs will count 25%.