Statistics 415. Forecasting. Prof. Rick Paik Schoenberg. S23. 

Lectures: Thu 6pm-8:50pm Young4216. 

Course webpage: http://www.stat.ucla.edu/~frederic/415/S26 . I am not maintaining the Canvas, CCLE, or Bruinlearn sites. See the site above for hw and other information. 

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 . We will also look a bit at Irizarry's Intro to Data Science, https://rafalab.github.io/dsbook . 

Office hours: Thu 5:30-5:55pm Slichter 3873 or by appointment. 

email: frederic@stat.ucla.edu . 

Statistics 415 will explore forecasting using primarily time series models and methods. The course will be very applied. Students will learn standard concepts in temporal and frequency analysis, followed by some more recent topics such as 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 with solid mathematical and statistical backgrounds. 

Grading: 

Midterm (20%) -- Thu May7, in class. 

Written project (20%) -- due Mon Jun1, 1159pm, via email. 

Oral Presentation (10%) -- Thu May28, in class.

Final exam (50%) -- Thu Jun 4, in class. 

Attendance in class is generally not mandatory and not counted as part of the grade. However, students must attend on the dates of the midterm, oral presentation, and final exam. 

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. Projects must be done independently and exam questions must be answered independently. On the exams, bring a pencil or pen, and any notes you want. Notes will be allowed, but no electronics can be used during the exams, including calculators, phones, tablets, or computers.  

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. 

Written Project:

Find a time-series dataset and analyze it using some of the relevant methods described in class. Your report should contain around 4 pages of text, followed by as many figures as appropriate. You may include as many figures as you like. 

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, emphasizing particular outliers or unusual results, and assessing the fit of any models used. 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 day of class before the final exam. These will involve simply presenting a clear and concise 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!!!