| Instructor: | Dr. Kristine Bell | Office: | S&T II, Rm. 145 | |
| email: | kbell@gmu.edu | Phone: | (703)-993-1707 |
| Time: | Mon. 7:20-10:00 p.m. |
| Place: | Enterprise Hall, Rm. 176 |
| Office Hours: | Mon. 5:00-7:00 p.m. or by appointment |
Description: 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 univariate time series analysis including mathematical modeling of time series data and methods for statistical inference. An integral part of the course is the use of R for simulation, calculation, and implementation of time series analysis techniques.
Advanced principles of time series analysis including mathematical modeling and methods for model identification and forecasting of nonstationary and seasonal time series data (ARIMA models), multivariate time series, and state-space models, are covered in the follow-on course STAT 758 offered in Spring 2009.
Prerequisite: STAT 544 or ECE 528 or equivalent multivariable calculus-based graduate course in Applied Probability or Random Processes.
Text: R. H. Shumway and D. S. Stoffer, Time Series Analysis and Its Applications With R Examples, 2nd. ed, Springer-Verlag, 2006. ISBN 978-0-387-29317-2. The website for the text is http://www.stat.pitt.edu/stoffer/tsa2/.
R: R is a freeware package that can be downloaded from the Comprehensive R Archive Network (CRAN). It is also available on the computers in the IT&E lab (S&T II, Rm. 137). No prior experience is assumed. The text provides examples in R and the associated website provides data files and R scripts that can be downloaded.
Blackboard: Course materials, announcements, etc. will be disseminated through Blackboard.
Schedule: This schedule is approximate and subject to change at any time by announcement in class. A more specific calendar will be maintained on Blackboard.
|
Class |
Date |
Quiz/ |
Assignment Schedule |
Material Covered |
|
1 |
8/25/08 |
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Chapter 1: Characteristics of Time Series (1.1-1.6) Chapter 2: Time Series Regression and Exploratory Data Analysis (2.1-2.4) Chapter 3: ARIMA Models (just the ARMA part) (3.1-3.6) Chapter 4: Spectral Analysis and Filtering (4.1-4.5, 4.7-4.8)
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|
9/01/08 |
Labor Day Holiday |
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2 |
9/08/08 |
Q1 |
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3 |
9/15/08 |
Q2 |
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4 |
9/22/08 |
Q3 |
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5 |
9/29/08 |
Q4 |
A1 |
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6 |
10/06/08 |
Q5 |
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7 |
10/14/08 |
Q6 |
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8 |
10/20/08 |
Q7 |
A2 | |
|
9 |
10/27/08 |
Midterm Exam |
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10 |
11/03/08 |
Q8 |
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11 |
11/10/08 |
Q9 |
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12 |
11/17/08 |
Q10 |
A3 |
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13 |
11/24/08 |
Q11 |
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14 |
12/01/08 |
Q12 |
A4 |
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12/08/08 |
Reading Day |
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15 |
12/15/08 |
Final Exam |
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Grading: Grades are based on total points earned from four components: Assignments, Quizzes, Midterm Exam, and Final Exam. Each component is worth 100 points.
Honor Code: All graded work is subject to the honor code. For the Quizzes and Exams, you will be expected to use only the allowed notes and materials, not give or receive assistance from anyone except the instructor, and stop work promptly when the test is over. For Assignments, you will be expected to use only the allowed notes and materials. Collaboration with other students currently enrolled in the course is allowed on Assignments.
Background:
Linear Systems. In this course, time series modeling and analysis techniques will be presented in the context of linear systems. We will cover the necessary linear systems material in the lectures. The Schaum's outline is a recommended reference. The others are classic texts for students who want or need more in-depth coverage of this material.
H. W. Hsu, Schaum's Outline of Signals and Systems, McGraw-Hill, 1995. ISBN: 978-0070306417.
A. V. Oppenheim and A. S. Willsky with S. H. Nawab, Signals and Systems, 2nd ed., Prentice Hall, 1996. ISBN: 978-0138147570. (Used for ECE 220 and 320)
A. V. Oppenheim and R. W. Schafer with J. R. Buck, Discrete-Time Signal Processing, 2nd ed., Prentice Hall, 1999. ISBN: 978-0137549207. (Used for ECE 535)
Applied Probability and Stochastic Processes. A solid
foundation in calculus, matrix algebra, applied probability and some knowledge of
stochastic (random) processes
and statistical inference is
assumed in the Shumway and Stoffer text. Mastery of calculus, matrix
algebra, and applied probability is essential for success in this course.
We will cover the necessary random processes and statistical inference
material in the lectures. The Schaum's outline is a recommended reference.
The others are classic texts for students who want or need more in-depth
coverage of this material.
H. W. Hsu, Schaum's Outline of Probability, Random Variables, & Random Processes, McGraw-Hill, 1996. ISBN: 978-0070306448.
S. Ross, A First Course in Probability, 7th ed., Prentice-Hall, 2006. ISBN: 978-0131856622. (Used for STAT 544)
C. Ash, The Probability Tutoring Book, Wiley/IEEE Press, 1993. ISBN 978-0780310513.