Course outline
STAT 443: Forecasting Fall 2020
Paul Marriott September 8, 2020
• Lecture Times: No synchronous lectures. All lectures to be posted online.
• Instructor: Paul Marriott (pmarriot@uwaterloo.ca) (M3 4204)
• Office Hours/Tutorials: 5:30-6:00pm Monday, Tuesday, Wednesday, Thursday, Friday
Course Description and Objectives: This course is an introduction to the area of statistical forecasting and control. It looks at how empirical information and appropriate modelling can be used to make forecasts and evaluate the associated uncertainty. It focuses on time series analysis and modelling, building on the student’s knowledge of regression. It looks at a number of different methodologies including linear filtering, the Box-Jenkin’s ap- proach to forecasting, Bayesian and state space method, Machine Learning, and frequency analysis. It will look at examples from a broad range of application areas.
By the conclusion of this course, students should have achieved the following objectives:
1. Be able to give a qualitative description of the structures of a wide range of types of time series, and discuss the following topics: time scales, trends, variability, season- ality, and other deterministic aspects, outliers, change points, stationarity and data generation mechanism.
2. Be able to use R to generate appropriate plots and summary statistics to communicate to others important aspects to a wide range of types of time series.
3. Be able to discuss, with a variety of examples, the importance of forecasting, pre- diction and control in real-world applications and how to quantify the effectiveness of differing methods. They will be able to discuss fundamental differences between short, medium and long term prediction.
4. Be able to select and use appropriate forecasting methodologies studied in the course to make forecasts based on data and appropriate subject matter information.
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5. Be able to quantify the uncertainties and assumptions associated with each of the forecasting and filtering methodologies studied in the class.
6. Be fluent in a number of R based tools which can be used to compute and summarise forecasts.
7. They should be able to describe the importance of statistical modelling in forecasting, prediction and control problems and master the mathematical techniques needed to exploit such models. They should be able to select, fit and critique such models.
Course Syllabus: The course will cover the following topics:
1. Introduction to forecasting, control and time series 2. Regression methods and model building principles
3. The theory of stationary processes
4. The Box-Jenkins approach to forecasting and control 5. Bayesian and state space methods
6. Evaluating methods of forecasting 7. The Kalman filter
8. ARCH and GARCH modelling
9. Other topics depending on time.
Learning Materials and course website: You can login to the course website at De- sire2Learn (Learn) using your Quest user ID and password. The following are the main sources of learning material
1. The course notes are the main source. These have been up-dated for this semester so please use the Fall 2020 version.
2. Recorded short talks. These will discuss specific parts of the course notes, details of computations and examples. They will not be exhaustive or cover everything in the notes.
3. Office hours are held through Virtual Classroom link under Connect tab on Learn. You may attend the office hours with your phone, tablet, or computer. If you would like to ask questions, you need to have a microphone. Having a webcam is not required for the office hours, but it is helpful to have one. The online platform will allow us to use a virtual whiteboard collaboratively
Some of these sessions will act more as tutorials discussing feedback from assignments and answering questions from Piazza. If they are not free sessions, open for students questions one-on-one, the sessions will be recorded.
I strongly encourage students to read broadly around the area. We will be looking at papers during the course and these are referenced in the Bibliography at end of my course notes. You might also find that it is helpful to look at a couple of freely available textbooks.
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1. Shumway, R. H. and D. S. Stoffer (2010). Time series analysis and its applications: with R examples. Springer Science & Business Media.
This can be found at https://www.stat.pitt.edu/stoffer/tsa4/tsa4.pdf
2. Rob J Hyndman and George Athanasopoulos, (2018) Forecasting: Principles and
Practice
This can be found at https://otexts.com/fpp2/
3. If you are looking for a specific research paper you might start with Google Scholar, https://scholar.google.ca. Sometime you will also need to log-in to the Univer- sity’s Library to get an open access copy.
When you are using materials you must reference them appropriately. A reasonable style can be seen in the course notes.
Q&A: Piazza (www.piazza.com) is an online Q&A platform, and will be widely used in this course. You are encouraged to submit questions you have about the course material on Piazza to discuss with your fellow classmates and TAs. If you ask questions about assignments which involve sharing any portion of your solutions, you MUST post your question privately.
The TAs will monitor Piazza regularly however if you require response from the instruc- tor I have added a tag named virtual office hour (VHO) to piazza. I will then, typically, cover this question and related issues during recorded office hours. You are welcome to participate in the live discussion particularly if you tagged the question for VHOs.
Course Assessment: There are four modes of assessment in this class:
1. Quizzes are held most weeks which does not have another assessments. These are timed, open book, multiple select or short answer type questions, directly related to statements in the courses notes and recorded lectures.
To prepare for such a quiz students should be familiar enough with the relevant parts of the course notes and the recorded lectures that they can quickly refer to the material the question is asking about.
2. Assignments are more substantial. Some of the questions here are naturally open ended and extend material from the notes.
It is allowed to work with others on an assignment but in this case you must submit a joint submission to Crowdmark. Students create their own groups. Any member of the group can submit on behalf of the group and a single group assessment will be graded. All students in the group will receive a copy of the graded feedback in their Crowdmark portfolio
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Once the assignment has been distributed, students will receive an email with a link to view the assignment. Students will be able to add or edit group members before submitting. Students can only add members who have not already been added to another group. Once a student is added to a group, the submission page is shared among the group members and any member can submit work on behalf of the group. Students will receive an email notification that they have been added to a group by a particular user
After the assignment deadline there will be a recorded feedback tutorial session dis- cussing what was expected for each question and the relationship between the ques- tion and the course as a whole.
3. The project will be individual work. It will be a combination of theory questions and applications. It will be more have more open ended material than the assignments, it will have more written work and covers the course as a whole.
4. The exam will be available during a 24 hour period for a two and a half hour window, see
https://uwaterloo.ca/registrar/final-examinations/exam-schedule
The weights across the different components are:
• 10% Combined Quizzes
• 30% Combined Assignments
• 20% Project
• 40% Online Exam. You are required to get a passing mark on the final exam to pass
the course.
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Tentative Timetable:
Week Starting
Topic Assignment Deadline
Notes
7 Sept 14 Sept 21 Sep 28 Sep 5 Oct 12 Oct 19 Oct 26 Oct 2 Nov 9 Nov 16 Nov 23 Nov 30 Nov 7 Dec
Chapter 1 Chapter 2 Chapter 2 Chapter 3 Chapter 3
Chapter 4 Chapter 4/5 Chapter 5 Chapter 5/6 Chapter 6 Chapter 7 Chapter 8
Quiz 1: 18 Sept Assignment 1: 25 Sept Quiz 2: 2 Oct Quiz 3: 9 Oct
Assignment 2: 23 Oct Quiz 4: 30 Oct Assignment 3: 6 Nov Quiz 5: 13 Nov Assignment 4: 20 Nov
Project: 7 Dec
Class starts (8 Sept )
Reading week Project distributed:
Class end (Dec 7)
Exam (TBA)
Table 1: Tentative Timetable: The following timetable is the plan (as of Sept 8th), but since the on-line structure is new to all of us changes may be made. In these circumstances announcements of changes will be posted on Learn as soon as practical.
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Marking Concerns: If you have concerns regarding the marking of an assessment, you may request your paper to be remarked within one week of the date you receive your marked paper, i.e. by 11:59pm (EST) 7 days after receiving your paper. No remark requests will be considered after the deadline. If you request a remark, your whole paper will be marked again and your grade may increase, decrease, or stay the same. All requests must be submitted via email to the instructor. If you request a remark, you must explain clearly which questions are marked unfairly and why you believe that you deserve more points in those questions.
Missed Quizzes and Assessments: Due to the very unusual circumstances of this term I will have a non-standard method of dealing with missed quizzes and assignments. Rather than having the usual documentation I will transfer the weight of any missed assignments automatically to the take home final, once you have emailed me, by email, of why you missed the assignment. The email must be sent to me in less than three days of the assignment being due.
To transfer the weight of the project I will require more formal documentation.
UW and Other Academic Policies:
1. At no point should any student post any course material including, but not limited to, recorded lectures, slides, practice problems, assignments, quizzes, solutions, etc. to any website except Learn and Piazza. All course material is the intellectual property of the instructor and the University of Waterloo. Stating or clicking a box indicating that you are the owner of this material is fraudulent. Students who are caught or suspected of sharing materials online will be reported to the Associate Deans Office.
2. A sample solution will be posted on Learn following each assignment due date. Please review these solutions before contacting the instructor with any marking concerns.
3. The free statistical package R will be used for data analysis. In particular, we will use RStudio throughout the course (available here for free download.) Throughout the semester we will discuss sample outputs so you can do the assignments and interpret the outputs appearing in the assignments, tutorial tests, and the final exam. R is available on the machines in the computer labs which you have access to. For those of you who would like to download R on your pc/laptop please visit R website and RStudio website.
4. A student who believes that a decision affecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating grievance. Read Policy 70, Student Petitions and Grievances, Section 4 here. When in doubt, please be certain to contact the departments administrative assistant who will provide further assistance.
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5. Persons with Disabilities: The office for AccessAbility (website here) collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with AAS at the start of each academic term.
6. Academic Integrity: In order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility. Grievance: A student who believes that a decision affecting some aspect of their university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70 – Student Petitions and grievances, Section 4. Discipline: A student is expected to know what constitutes academic integrity, to avoid committing an academic offence, and to take responsibility for their actions. When misconduct has been found to have occurred, disciplinary penalties will be imposed under Policy 71 – Student Discipline. For information on categories of offences and types of penalties, students should refer to: Policy 71 – Student Discipline.
7. Avoiding Academic Offences: For information on commonly misunderstood academic offences and how to avoid them, students should refer to the Faculty of Mathematics Cheating and Student Academic Discipline Policy.
8. Extenuating Circumstances: Given the current circumstances, the load on all online platforms (including Learn) is higher than before. As a result, if Learn faces sig- nificant issues during the semester, we may have to change the Quiz portion of the assessments to a different assessment strategy.
Mental Health Support:
The Faculty of Math encourages students to seek out mental health support if needed. There are the following on campus resources.
1. Campus Wellness https://uwaterloo.ca/campus-wellness/
2. Counselling Services: counselling.services@uwaterloo.ca/ 519-888-4567 ext 32655
3. MATES: one-to-one peer support program offered by Federation of Students (FEDS) and Counselling Services: mates@uwaterloo.ca
4. Health Services: located across the creek from the Student Life Centre, 519-888-4096.
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