Syllabus.  
Stat 221, Time Series Analysis. <br>
Prof. Rick Schoenberg. <br>

Fall 2022. <br><br>

Lectures:  MW 930-1045am in Boelter 5422. <br><br> 

Office Hours:  Wed 11am-1145am, Slichter 3873. <br><br>

Email:  frederic@stat.ucla.edu<br><br>

Course webpage:  <a href="http://www.stat.ucla.edu/~frederic/221/F22">
http://www.stat.ucla.edu/~frederic/221/F22 </a>. 
<br><br> 

I am not maintaining the CCLE or Canvas site. See the site above for hw and other information. </a><br><br>

Textbook: Time Series Analysis and Its Applications, With R Examples, 4th Edition, by
R. Shumway and D. Stoffer.  Springer, NY 2017. 
http://www.stat.pitt.edu/stoffer/tsa4 .
<br><br> 

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.<br><br>
  
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.<br><br> 

Grading: <br>
Exam (40%), Nov7, in class. <br>
Oral Presentation (10%) -- last 4 lectures.<br>
Written project (50%) -- due Fri Dec 9, 1159pm, by email to me. <br><br> 

Attendance in class is generally not mandatory and not counted as 
part of the grade.  However, the last 4 lectures of class are an exception: 
all students MUST attend class, on time, for these final days.<br><br>

There will most likely 
be no extensions for the project, presentation, or hws. 
Students who are unable to make these dates or otherwise 
fulfill the course requirements
must consult with the instructor in advance, if possible.
<br><br>

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. 
<br><br>

Written Project:<br>
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.<br><br>

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).
<br><br>

Oral presentations of project results will take place on the last 2 weeks of 
class.  These will involve simply presenting a clear and 
concise 5-6 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 probably only show around 5-6 figures in your 
presentation. 
<br><br>