Indiana University Bloomington

Every Year

Statistics S650
Time Series

Contact: Jerome Busemeyer
Class Time: afternoon
Class Days: Tu, Th
Capacity: 20
Pre-Requisites: linear models
Instructor: Jerome R Busemeyer
Days Per Week Offered: 2
Recommended follow-up classes: time series 2
Syllabus: No Syllabus Avaliable
Keywords: linear dynamics, stochastic processes, auto regression, state space models
Description: Purpose: Introduce students to linear dynamic systems theory, statistical models for time series data, and statistical tools for signal processing. Learn how to use time series and signal processing programs to analyze research data.
Method: Basic theory and computer examples will be presented by the instructor during lectures. Approximately biweekly homework assignments will be completed by students. These will be 5 minute classroom presentations by students.
Books: Shumway, Robert H., & Stoffer, David S. (2006) Time series analysis and its applications. Springer
Substantive Orientation: practical
Statistical Orientation: practical
Applied/Theoretical: applied
Formal Computing Lab: Yes
Software: MatLab, R, Stata
How Software is Used: bi weekly
Exams: no