st525.html

ST 525 Time Series I

Instructor:: Richard Davis , Dept of Statistics
201 Statistics Building, 1-7321
rdavis@stat.colostate.edu

Prerequisite: ST 430.

Required Text: Time Series: Theory and Methods, 2nd Edition 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:

readme.doc (revised 8-30-96) (Installation instructions for ITSM96.)
unzip.exe (8-30-96) (Utility to uncompress ITSM96 and/or ITSM6.)
itsm96.zip (8-30-96) (Access limited.) After the itsm96.zip and unzip.exe files have been downloaded, put both files in your root directory on the C drive and issue the command C:\> unzip itsm96 which will uncompress the program and data files in a newly created subdirectory called itsm96. For further directions, read the readme.doc file located in the itsm96 subdirectory. --> See me if you need to get a copy of ITSM96.
itsm6.zip (12-5-97) (Access limited.) This version of ITSM is under development and requires a PC running Windows 95 or Windows NT. We would appreciate your comments and help in terms of both debugging (reporting errors) and design of the software. Give this version a test spin--it's fun!! After the itsm6.zip and unzip.exe files have been downloaded, put both files in your root directory on the C drive and issue the command C:\> unzip itsm6 which will uncompress the program and data files in a newly created subdirectory called itsm6. There are only 3 executable files (pest.exe, smooth.exe, and forecast.exe) and all the data files have the extesnion .TSM. Run the programs directly from the C:\> prompt or from the RUN selection under the START button on WINDOWS 95.
ITSM for the workstation This postscript document gives instructions for the use of ITSM on the stat department's workstations.

Time:

MWF: 1:10-2:00, E206 Engineering

Office hours: MW: 2-3

Other References:

Topics to be Covered in Course:

Examples, objectives, general approaches. [1.1,1.2]
Removing trend and/or seasonality. [1.4]
Stationary random processes: the autocorrelation function; the sample mean and sample autocorrelation function; applications of Bartlett's formula (Theorem 7.2.1) to white noise, MA(1), and AR(1) processes. [1.3, 1.5, 7.1, 7.2]
Hilbert spaces, stationary processes and best linear mean square prediction. [2.1-2.3]
ARMA processes and their autocorrelation and partial autocorrelation functions. [3.1-3.4]
Introduction to spectral theory and linear filtering. Spectral densities of ARMA processes. [4.1-4.4]
Recursive prediction of ARMA processes. [5.1-5.5]
Parameter estimation for ARMA processes. [8.1-8.9]
Model-building with ARIMA processes. [9.1-9.6]

Course Assignments:: HW 1 (Due 9-9): 1.1-1.4, 1.7; HW 2 (Due 9-16): 1.5-1.7, 1.9, 1.10, 1.11; HW 3 (Due 9-23): 1.8, 1.12, 1.13, 2.1, 2.3; HW 4 (Due 9-30): 2.4, 2.5, 2.6, 2.10, 2.12; HW 5 (Due 10-7): 3.1, 3.3, 3.8, 3.10, 3.12; HW 6 (Due 10-21): 3.13--3.16, 3.20, 3.21; HW 7 (Due 10-28): 5.1,5.3,5.8,5.10,5.11; HW 8 (Due 11-4): 7.1--7.3,8.1,8.2; HW 9 (Due 11-11): 8.5,8.6,8.15 (You may use only 10 realizations for; HW 10 (Due 12-2): 8.8,8.18,8.19,8.20,8.23 8.5,8.6.); HW 11 (Due 12-6): 9.2,9.4,9.5,9.6; HW 12 (Due 12-13): 9.7,9.8,9.10,9.11,9.13(Series B (appb) and C (appc) only)

Grading:

Midterm schedule:

Midterm 1: October 9, 1996

Midterm 2: November 18, 1996