ST 525 Time Series I (***MEET IN
B101 ENGINEERING WEDNESDAY 10/24***)
Time and Place:
MWF: 1:10-2:00, Engineering E206
Instructor:
F. Jay
Breidt, Associate Professor of Statistics
201 Statistics Building, (970)-491-6786, FAX: (970)-491-7895
mailto:jbreidt@stat.colostate.edu,
http://www.stat.colostate.edu/~jbreidt /st525/
Office hours: MWF 2.00
- 3.00 or other times by appointment (email works well for scheduling an
appointment or getting a quick answer).
Prerequisite: ST 430.
Required Text: Introduction to Time Series and Forecasting by
P.J. Brockwell and R.A. Davis
Optional Text: ITSM for Windows by P.J. Brockwell and R.A.
Davis.
- Software for the course:
A CD containing the time series package ITSM2000, is contained in
the text (an older version, itsm96, is also on the CD but will not be
used). Installation instructions for ITSM2000, which requires a PC running
Windows 95, Windows NT or a more recent version of Windows, are given at
the back of the text. On campus you can download the package by clicking
on itsm6pro.zip. The program and data files will unzip
in a newly created subdirectory called itsm2000. There is only one
executable file and all the data files have the extension .TSM. Run the
program by double clicking on the ITSM icon. When the ITSM window opens
click on Help>Contents>Getting Started for more detailed information
on the program.
ITSM2000 is already installed on the Statistics Department network.
Handouts: Supplementary notes may be posted on the web as PDF files.
Other References:
- Time Series: Theory and
Methods, 2nd Edition, Brockwell and Davis
- The Analysis of Time
Series, An Introduction, Chatfield
- Introduction to
Statistical Time Series, Fuller
- Time Series Analysis:
Forecasting and Control, Box, Jenkins and Reinsel
- Time Series Analysis and
its Applications, Shumway and Stoffer
Topics to be Covered in Course:
- Examples, objectives, general
approaches, stationary models. [1.1,1.2, 1.3, 1.4]
- Removing trend and/or
seasonality, testing an estimated noise sequence. [1.5, 1.6]
- Stationary random processes:
basic properties, linear processes, introduction to ARMA processes,
properties of the sample mean and autocorrelation function. [2.1, 2.2,
2.3, 2.4]
- Forecasting stationary time
series, the Wold decomposition. [2.5, 2.6]
- ARMA models: ARMA(p,q)
processes, the ACF and PACF, forecasting ARMA processes [3.1-3.3]
- Introduction to spectral theory
and linear filtering. Spectral densities of ARMA processes. [4.1-4.4]
- Modelling and forecasting
with ARMA processes. [5.1-5.5]
- Non-stationary and seasonal
time series models: ARIMA models, identification, unit roots, seasonal
models, regression with time series errors. [6.1-6.6]
Course Assignments:
Grading:
|
Homework
|
25%
|
|
Exams 1 and 2
|
40%
|
|
Final Exam
|
35%
|
Calendar Notes:
Midterm 1: September 21 (tentative)
Midterm 2: October 26 (tentative)
Thanksgiving break: November 19 –
November 23
Finals week: December 10 – December
14