Join our comprehensive web course led by Dr. McElroy, Senior Time Series Mathematical Statistician at U.S. Census Bureau and co-author of the book “Time Series: A First Course with Bootstrap Starter” (Chapman and Hall/CRC, 2020). Whether you're new to time series analysis or looking to expand your knowledge, this course is the perfect opportunity to learn fundamentals of analyzing time series data, while including a few non-standard concepts such as entropy, volatility filtering, and time series bootstraps. The course will incorporate coding in the powerful statistical software R of all concepts, methods, and examples. Don’t miss out on this chance to enhance your skills with guidance from a seasoned expert in time series analysis.
The full course consists of twelve 1-hour webinars. Registration is now open for the first four webinars. Those who register will receive free access to the remaining webinars.
Dates: Thursdays in April: 3, 10, 17, and 24, 2025 (four 1-hour sessions)
Time: 11:00 a.m. – 12:00 p.m. ET
Format: Live, virtual via Zoom. R notebooks available for free on the course's GitHub site.
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Registration Fees:
Business & Economic Statistics Section Members: $20 [Join here]
ASA Members: $30
Student ASA Members: $25
Nonmembers: $45
Lecturer Information: Tucker S. McElroy is Senior Time Series Mathematical Statistician at the Research and Methodology Directorate, U.S. Census Bureau, where he has contributed to developing time series research and software for the last 21 years. He has published more than 100 papers and is a recipient of the Arthur S. Flemming award (2011).
Full Course Description: This course provides an introduction to the fundamentals of time series analysis, designed for individuals holding a bachelor's or master's degree in statistics. Participants will obtain a basic knowledge of time series theory and methodology. They will be able to analyze time series data by exploratory analysis, by model identification and fitting, and by making applications such as forecasting. They will know how to use R to perform these tasks: not only to apply common time series functions appropriately, but also to write R scripts that capture time series methodology. In addition to learning basic time series topics, including ARMA modeling, the course also explores advanced techniques, such as entropy, volatility filtering, and time series bootstrapping.
Prerequisites: The intended audience includes statisticians with little or no knowledge of time series, but a general knowledge of statistics. Prerequisites include a course on linear models, a course on mathematical statistics (such concepts as bias, variance, and the Gaussian distribution), and a familiarity with linear algebra (the transpose, inverse, and eigen-values of a matrix).
If you have any questions about the webinars, please send an email to yao.zheng@uconn.edu