STA457H1 S-LEC5101 Time Series Analysis Winter 2022
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Time Series Analysis
STA457H1 S-LEC5101
STA457H1/STA2202H S-LEC0101 & STA457H1S LEC2001
Winter 2022
1 Course Content
This course will be offered entirely online, with a combination of synchronous lectures and asynchronous video lectures. The majority of the course content will be uploaded to Quercus as pre-recorded videos to be watched prior to the synchronous meetings. Further, any important announcements will also be posted in Quercus. Please make sure to check it regularly so you don’t miss anything.
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https://q.utoronto.ca/courses/236313 The synchronous classes will occur through Zoom and will focus on additional materials, worked examples and demonstration of concepts using the R statistical software. This is a fully online course. Live class sessions, tests, and quizzes will be held via Quercus. Students are responsible for ensuring that they have reliable internet. |
Course materials provided on Quercus are for the use of students currently enrolled in this course only. Sharing (e.g., posting, providing, selling) course materials with
anyone outside of the course is considered unauthorized use.
Lectures:
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We will use a mix of synchronous learning and asynchronous learning. Lecture slides, along with asynchronous video lectures, will be uploaded weekly. We will use the scheduled lecture times for problem-solving, live question-answer(QA) sessions, and biweekly 30-45 minutes quizzes. Tutorials are held during the regular scheduled tutorial time. During the tutorial, you will be given an R-related problem to work on with the help of the TA or apply the concepts learned in the lectures. Make sure to follow any instructions that your instructor has written and leave enough extra time before the due date to submit your work successfully. |
2 Course Description
An overview of methods and problems in the analysis of time series data. Topics include: de- scriptive methods; filtering and smoothing time series; theory of stationary processes; identifica- tion and estimation of time series models; forecasting; seasonal adjustment; spectral estimation; bivariate time series models.
Course Prerequisites
See Academic Calendar to learn more about course prerequisites.
Course Objectives/Learning Outcomes
By the end of this course, all students should have a solid understanding of methods and problems in analyzing time series data with a primary application in Economics, Business, Finance, Physical and Environmental Sciences. The course will cover theoretical and practical aspects of time series analysis, making extensive use of the R statistical software.
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Understand and reason with the basic time series concepts Interpret and compare different time series models Identify and model different types of time series data Perform time series modelling/forecasting and present the results Use R to construct time series models and conduct analysis |
3 Course Materials
Textbook:
Time Series Analysis and Its Applications
With R Examples, 4th Edition
by Robert H. Shumway & David S. Stoffer.
ISBN 978-3-319-52451-1
ISBN 978-3-319-52452-8 (eBook)
Statistical Software:
We will be using RStudio for performing statistical analyses. R is a free software that can either be downloaded onto your personal computer or used in the cloud. If you choose to work with R on your personal computer, then installation will be a two step process:
● The base R framework is available for download at http://cran.r-project.org for Windows, Mac and Linux operating systems.
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Next, RStudio is a good integrated development environment to R (makes it simpler to work in R) and can also be downloaded for free at https://www.rstudio.com/products/ rstudio/download. |
For each tutorial, it would be required that you submit a reproducible RMarkdown file with your codes and a knitted RMarkdown document as your data analysis report. To learn more about RMarkdown, refer to https://rmarkdown.rstudio.com/index.html.
2022-01-20