Syllabus.
Stat 221, Time Series Analysis.
Prof. Rick Paik Schoenberg.
Winter 2017.
Lectures: TTh 930-1045am, Royce 154.
Office Hours: Tue 5-6pm, Math Science 8965.
Email: frederic@stat.ucla.edu
Course webpage:
http://www.stat.ucla.edu/~frederic/221/W17 . I am not maintaining the CCLE site. See the site above for hw and other information.
Textbook: Time Series Analysis and Its Applications, With R Examples, 3rd Edition, by
R. Shumway and D. Stoffer. Springer, NY 2011.
http://www.stat.pitt.edu/stoffer/tsa3/tsa3.html .
Statistics 221 will explore the methods used in the analysis of
numerical time series data. The course will be both theoretical and
applied. Students will learn standard concepts in temporal and frequency
analysis, followed by some more recent topics such as wavelets and modern spectral analysis techniques.
Examples will be provided throughout the instruction of the course, and
students will implement the techniques discussed in class, using real data
sets. Dedicated students should come away with an in-depth understanding
of statistical concepts related to time series, as well as a thorough
comprehension of how and when
to implement various techniques in practice.
The course is designed for graduate students in statistics or
mathematics, and may also be taken by students from other disciplines
provided those students have sufficient mathematical and statistical
backgrounds.
Some experience in statistical computing is recommended.
Grading:
Homework (40%)
Oral Presentation (10%) -- last 2 weeks of class.
Written project (50%) -- due Tue, Mar 21, by email to me.
Attendance in class is generally not mandatory and not counted as
part of the grade. However, the last week of class is an exception:
all students MUST attend class, on time, for the final week.
There will most likely
be no extensions for the project or presentation.
Students who are unable to make these dates or otherwise
fulfill the course requirements
must consult with the instructor in advance, if possible.
Hw problems must be solved independently. Hw should be handed in at the very beginning of class when it is due. Late hws will not be accepted.
Homeworks must be submitted in hard copy, rather than by email or fax.
I am almost always open to questions, but one question I would like you not to ask me is "What did we do in class?" If you miss class, please get caught up on the notes from a fellow classmate.
Homework 1 is due Tue Jan 24, in the beginning of class, and asks you to list the names and email addresses of two other students in this course, and do problems
1.3, 1.10, 1.19, 1.20, and 1.26.
On problem 1.10, where it says "and autocorrelation function" in line 2, it should be autocovariance function.
Written Project:
Find a time-series dataset and analyze it using some of the relevant methods
described in class. Your report should contain 4-6 pages
of text, followed by as many figures as appropriate. You may include as
many figures as you like, but the text itself should not exceed 6 pages,
double-spaced.
In selecting your dataset
choose something that interests you, and try to have a main, answerable question
in mind.
Begin your paper with an introduction, a description of your data and how
they were obtained, and a summary of the main question(s) to be addressed
in your paper. Then summarize your analyses, paying special attention
to any assumptions you are making and the plausibility of those assumptions.
Conclude by assessing how effective the methods you used were in helping
to answer your main question(s).
Oral presentations of project results will take place on the last 2 weeks of
class. These will involve simply presenting a clear and
concise 8-10 minute summary of your dataset including a couple of your main results.
Do not try to show all the results from your paper in your oral
presentation!!! You should only show around 4-6 figures in your
presentation.