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Introducing the NISS Short Course Series: Foundations of Time Series Analysis with R with Tucker S. McElroy

  • 1.  Introducing the NISS Short Course Series: Foundations of Time Series Analysis with R with Tucker S. McElroy

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    Visit Short Course Website: NISS Short Course Series: Foundations of Time Series Analysis with R with Tucker S. McElroy | National Institute of Statistical Sciences

    Duration: 12 one-hour sessions | Schedule: Wednesdays, April 1 - June 17, 2026 - 12:00pm to 1:00pm ET on Zoom

    Eventbrite Registration: https://www.eventbrite.com/e/niss-short-course-series-foundations-of-time-series-analysis-with-r-tickets-1984645091895?aff=oddtdtcreator

    Overview

    Across twelve one‑hour sessions, participants will walk through the full content of Time Series: A First Course with Bootstrap Starter, with each session dedicated to one chapter of the book. This series highlights the opportunity to engage in live, hands‑on R coding tutorials taught directly by the author himself: Tucker S. McElroy, Senior Time Series Mathematical Statistician at the U.S. Census Bureau.

    Participants will implement each concept in real time as Tucker McElroy demonstrates methods, walks through examples, and offers practical insights based on more than two decades of experience in time series research. Attendees will also have the unique opportunity to ask questions during every session, making this course an exceptional chance to learn directly from one of the leading experts in the field.

    Key Features

    • Explore edge cases and deepen conceptual understanding

    • Debug R code with real-time guidance

    • Learn fundamental time series topics, including: stationarity, autocorrelation, spectral analysis, filtering and forecasting

    • Gain exposure to non‑standard concepts such as: entropy, volatility filtering and time series bootstraps

    • Benefit from a blend of conceptual learning, author‑led coding, and live Q&A

    • Experience a practical, engaging, and accessible format ideal for those new to time series

    Schedule

    This series will run for 12 one-hour sessions virtually on zoom, once a week on Wednesdays, April 1 – June 17, 2026, from 12–1 pm ET.

    Registration Details

     
    • Full short course series purchase: $300
     
    • Individual short course ticket purchase: $35 each
     
    NISS Affiliates: Please reach out to Megan Glenn (mglenn@niss.org) for your discount promo code. Ensure your email is through a valid affiliate institution. See our NISS Affiliates list here: NISS Affiliates | National Institute of Statistical Sciences

    About the Instructor

    Tucker S. McElroy is Senior Time Series Mathematical Statistician at the U.S. Census Bureau, where he has contributed to developing time series research and software for the last 22 years. He has published more than 100 papers, is a fellow of the American Statistical Association, and is a recipient of the Arthur S. Flemming award (2011). Visit GitHub Profile

    Abstract

    This short course series will cover the content of the book Time Series: A First Course with Bootstrap Starter in twelve sessions, each session covering a chapter.
    The course will be delivered to the National Institute of Statistical Sciences (NISS). 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).
    The aim is to cover basic concepts of time series analysis at a level suitable for those with a bachelor's or master's degree in statistics, while including a few non-standard concepts such as entropy, volatility filtering, and time series bootstraps. A second aim is to incorporate coding in R of all concepts, methods, and examples.
    View Course Syllabus Link on GitHub


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    Randy Freret
    NISS.org
    rfreret@niss.org
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