Macrosystems EDDIE Module 11: Time Series Modeling and Prediction of Environmental Data (Instructor Materials)

This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 11: Time Series Modeling and Prediction of Environmental Data, a ~3-hour educational module for undergraduates. Advances in environmental sensor technology in recent decades are enabling the collection of environmental data at high temporal frequencies (e.g., every 10 minutes) across many ecosystems. These time series of environmental data can be used to gain information about previous and current conditions of ecosystems, as well as make predictions about ecosystem conditions in the future. Researchers and managers commonly apply a range of time series models to understand the complex patterns that can occur in high-frequency environmental time series data. These models use statistical and machine learning methods to identify signals in high-frequency environmental data. In this module, students will apply several different time series and machine learning models to explore, analyze, and interpret environmental data. They will explore data from an environmental case study of their choice, choose which environmental variables to use to fit a time series model, assess the model, and apply the model to make predictions of future ecosystem conditions. Then, students will process a new dataset into a standardized format, upload it into the module's R Shiny App, and fit several other models to compare predictive performance across models. Students will also evaluate the ecological understanding that can be gained from each model (e.g., which driver variables are important for explaining the dynamics of the target environmental variable being predicted). The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. Students complete module activities using an R Shiny web application which can be accessed from an internet browser on a computer. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module11/. A GitHub repository is available for the R Shiny application code (https://github.com/MacrosystemsEDDIE/module11), and the code repository has been published with a DOI to Zenodo (https://doi.org/10.5281/zenodo.18841929). This data package includes open source versions of module introductory lecture slides, a student handout, and an instructor manual which can be used to teach the module. Readers are referred to the module landing page for additional information (https://serc.carleton.edu/eddie/teaching_materials/modules/module11.html).