CSI 678 / STAT 658: Time Series Analysis and Forecasting
James Gentle
Time series analysis is used for diverse applications in economics,
the social sciences, the physical and environmental sciences, medicine,
and signal processing.
This course presents the fundamental principles of
time series analysis including mathematical modeling of time
series data and methods for statistical inference. Models in both the
time domain and the frequency domain will be developed and explored.
Course Objectives:
At the end of this course the student should be familiar with the basic
concepts of time series analysis and forecasting. The student should be
able to use standard computer software to analyze time series to perform
simple forecasts. The student should also be prepared to take more
advanced courses in time series.
Topics
- Stationary and nonstationary processes
- Autoregressive processes
- Moving average processes
- ARIMA processes
- Autocorrelation and partial autocorrelation functions
- Spectral density functions
- Identification of models
- Estimation of model parameters
- Forecasting techniques
Prerequsites
STAT 544 or ECE 528 or equivalent
multivariable calculus-based graduate course in Applied Probability or
Random Processes.
Grading
homework assignments (30)
midterm exam (30)
final exam (40)
Each homework will be graded based on 100 points, and 5 points will be deducted
for each day that the homework is late.
Software
The main analysis software used in the course will be R. No prior experience
with R is assumed.
Information about R, including links for downloading, can be obtained at
http://www.r-project.org/