Introduction to Time Series
Instructor: David Brillinger, brill@stat.berkeley.edu
Office hours: Wednesday 15:00 - 17:30 in 417 Evans
Meant for graduate students in Statistics and other Departments.
GSI: Srikesh G. Arunajadai, srikesh@stat.berkeley.edu
Office hours: 8:00 - 10:00 on Friday
Syllabus:
Random process data analysis: frequency- and time-side analysis of ordinary time series, stationarity and non-stationarity, parametric and nonparametric (ARIMA, GARCH, ...), Markov chains, point processes, spatial processes, spatial-temporal processes, ...
These topics will be presented in a comparative fashion
Classes: Tu Th 15:30 - 17:00 in 330 Evans
Lab Section: F 10:00 - 1200 in 344 Evans
Course Homepage: www.stat.berkeley.edu/~brill/Stat248
Texts: D. R. Brillinger, Time Series: Data Analysis and Theory, SIAM.
P. Guttorp, Stochastic Modelling of Scientific Data, Chapman and Hall.
Prerequisites: Statistics 101&102 or 134&135 or equivalents.
The course work will be directed towards the students preparing an analysis of pertinent scientific data using the methods covered in the course.
The grade will come from a combination of a Project Proposal and an Independent Project.
Some specifics of the course and the project
1 November 2007