Applied Time Series Analysis

STA2530, Fall 2023

Wednesday 10:00 am - 1:00 pm, LOCATION: Room 9016; Office Hours: Wednesday 1:30 pm - 2:30 pm
TA Session: Tuesday 11:00 am - 12:00 pm

COVID-19 CORONAVIRUS: The policies set forth in this course are subject to change as we try to determine how best to keep you safe from the COVID-19 coronavirus while we provide the education we promised you.

Instructor: Xiaofei Shi xf.shi[at]utoronto(dot)ca

TAs

Course Description: An overview of methods and problems in the analysis of time series data related to finance and insurance. The course will focus on both theory and application with real datasets using R and Python and will require writing reports. Topics include stationary processes, linear processes; elements of inference in time and frequency domains with applications; A RMA, ARIMA, SARIMA, ARCH, GARCH; filtering and smoothing time series; and State-space models.

Course Prerequisites: Statistics, (Calculus based) Probability, Linear Regression Models, Linear Algebra.

Recommended Readings:

Introductory Econometrics for Finance, Chris Brooks.
The Econometrics of Financial Markets, John Y. Campbell, Andrew W. Lo, Archie C. Mackinlay.
Time Series: Theory and Methods, Peter J. Brockwell, Richard A. Davis.
Time Series Analysis, James D. Hamilton.

Homework: There will be 4 homework assignments, approximately evenly spaced throughout the semester. The homework will be posted on Quercus. We will also use Quercus for submitting and grading. We highly recommend using the discussion function on Quercus for discussion. Homeworks submitted after the deadline will not be considered, so please plan in advance. In the case of an emergency (sudden sickness, family problems, etc.), a reasonable extension will be assigned. But we emphasize that this is reserved for true emergencies.

Evaluation: 60% for Homework Average + 15% for Class Presentation + 25% for Report.
Schedule

Date	Topic	Note
09/13	Introduction	HW1 out
09/20	Basic Concepts of Time Series
09/27	Machine Learning Methods for Time Series I	HW1 due; HW2 out
10/04	Machine Learning Methods for Time Series II
10/11	Classical Time Series Models I	HW2 due; HW3 out
10/18	Classical Time Seires Models II
10/25	Classical Time Seires Models III	HW3 due; HW4 out
11/01	Impulse Response Analysis
11/08	Forecasting and Filtering of Time Series	HW4 due
11/15	Introduction to Reinforcement Learning
11/22	Class Presentation I
11/29	Class Presentation II

Policy on Late Homework: Each homework will have approximately four problems. Each homework is due at midnight on due date. All homework submitted late is not going to be graded and you will receive zero credit for that homework.

Policy on Collaboration: You are encouraged to work together on the homework. Discussing the homework problems with one another can be a valuable learning experience. However, it is a violation of the rules on academic integrity to copy another student's solution and submit it as your own. You should write up your solutions separately, not referring to a common document. Furthermore, you should not submit any work that you do not fully understand. You should be able to start with a clean sheet of paper and without notes or assistance write out the solution to any homework solution you submit. If you will do that with every homework you submit, the similarity between your solutions and those of other students will not arouse suspicion. More importantly, you will be well prepared for the interviews. You are not permitted to use homework solutions for this course from previous years or solutions you find from other sources, including the internet. If multiple students turn in identical solutions, all of them will receive a zero. People caught cheating generally tell a story about being unable to keep up with the material. First of all, remember that you can submit homework late. Secondly, if there is something going on in your life that makes it hard for you to keep up, please let me know. That brings me to the following point.

Take Care of Yourself: It is easy for me to say and hard for all of us, including me, to do, but taking care of your physical and mental health is essential, especially during the COVID-19 pandemic. Life is a marathon, and you need to pace yourself. Do your best to maintain a healthy lifestyle by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress. If you or anyone you know experiences extreme academic stress, difficult life events, or feelings of anxiety or depression, I strongly encourage you to seek support. Counseling and Psychological Services is here to help 24/7, and everything will be confidential: https://mentalhealth.utoronto.ca/
In addition, consider reaching out to a friend, faculty or family member you trust for help getting connected to the support. Keep in mind that for serious psychological issues, the first counselor you meet with may not be the right one for you, but this does not mean you should give up on counseling. Keep looking for someone who can help you.

If you or someone you know is feeling suicidal or in danger of self-harm, call immediately, day or night:
Non-urgent campus police: 416-978-2323
If the situation is life threatening, call the police:
Urgent campus police: 416-978-2222
Off campus: 911

If you have questions about this advice, your coursework, or anything else about which I might be helpful, please let me know.