Introduction to Time Series
Subtitle: Random Process Data Analysis and Methods
Meant for graduate students in Statistics and other Departments.
Instructor: David Brillinger, brill@stat.berkeley.edu
Office hours: Wednesday 15:00 - 17:30 in 417 Evans
GSI: Irma Hernandez, ihernan@stat.berkeley.edu
Office hours:TBA
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 2:00 - 3:30 in 332 Evans
Lab Section: Fri 12:00 - 2:00 in 344 Evans.
Course Homepage: www.stat.berkeley.edu/~brill/Stat248
Supplimentary 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 Lab will be directed to teaching the pertintent material concerning the statistical package R, particularly the time series functions, and to helping the students with realizing their projects.
The grade will come from a combination of a Project Proposal and an Independent Project.
Some specifics of the course and the project
18 April 2009