Schedule

Italics indicate that laptops are required for lab activities.

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• Aug. 29
  • Lecture: The forecasting workflow
  • Lab: Set up R, Intro to R assignment

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• Aug. 31
  • Discussion: Why dynamics and forecasting?
    • Reading: Clark et al. 2001
      • Discussion questions
    • Reading: Houlahan et al. 2017
      • Discussion questions
  • Lab: Intro to R assignment

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• Sept. 5
  • DUE: Before class install the latest version of R, the latest version of RStudio, and these R Packages: forecast, ggplot2, lubridate, dplyr, scales, gridExtra, ggthemes, reshape2, zoo
    • Reading: Look through the following NEON Working with Time Series Data Tutorials
      • Time Series 02: Date-time conversions
      • Time Series 04: Manipulate data with dplyr
      • Time Series 05: Plot time series with ggplot2
      • Also, Julian day conversion
  • Lecture: Working with time series data
  • Lab: Time series practice
  • Optional Weecology video lecture: Dates/times

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• Sept. 7
  • Lab: Time series decomposition and autocorrelation assignment
  • Optional Weecology video lecture: Time series decomposition
  • Optional Weecology video lecture: Time series autocorrelation

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• Sept. 12
  • DUE: Time series decomposition and autocorrelation assignment
  • Lecture: Introduction to time series modeling demo code
  • Optional Weecology video lecture: ARIMA

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• Sept. 14
  • Discussion: Introduction to forecasting
    • Reading: Forecasting Principles & Process Chapter 1
      • Discussion questions
  • Lab:
    • Introduction to forecasting demo code
    • Introduction to forecasting assignment

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• Sept. 19
  • Lecture: Time series and population models
  • Lab:
    • Work on Introduction to forecasting assignment

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• Sept. 21
  • DUE: Introduction to forecasting assignment
  • Discussion: Understanding vs. prediction
    • Reading: Breiman 2001(skim the examples)
      • Discussion questions
  • Discussion: Understanding vs. prediction and model selection
    • Reading: Tredennick et al. 2021
      • Discussion questions

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• Sept. 26
  • Mini-lecture: R syntax, equations, and coefficients (Michael Stemkovski)

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• Sept. 28
  • Discussion: The importance of uncertainty
    • Reading: Dietze Chapter 2
      • Discussion questions
  • Simulating prediction intervals

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• Oct. 3
  • Forecast evaluation demo code
  • Lab:
    • Work on Evaluating forecasts assignment
    • Optional Weecology video lectures (search their YouTube)

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• Oct. 5
  • Presentation: Weather forecasting (Peter)
  • Work on Evaluating forecasts assignment

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• Oct. 10
  • DUE: Evaluating forecasts assignment
  • Lecture: Bias-variance trade-off
  • Lab: Begin Forecasting Challenge #1

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• Oct. 12
  • Presentation: Carbon cycle (Peter)
  • Lab: Begin Forecasting Challenge #1

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• Oct. 17
  • Presentation: Forecasting phenology (Annie)
  • Lab: Work on Forecasting Challenge #1

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• Oct. 19
  • Presentation: Population dynamics (Maria), perhaps animals or plants
  • Lab: Work on Forecasting Challenge #1
  • Example code for regularization

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• Oct. 24
  • Presentation: Invasions (Elsbeth)
  • Lab: Work on Forecasting Challenge #1

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• Oct. 26
  • DUE at 9:00 am: Forecasting Challenge #1
  • Scores calculated, winners announced!
  • Debrief the challenge

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• Oct. 31
  • DUE: Read up on NEON Forecast Challenge and check out this video about it (second half more relevant than the first)
  • Decide as a group on next forecasting challenge. NEON? FLU?
  • Presentation: Political forecasting (Faraz)

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• Nov. 2
  • Presentation: Adaptation (Brian)
  • DUE: Read Intro to Bayes
  • Lecture: Bayesian modeling: background

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• Nov. 7
  • Presentation: Economic forecasting (Silver chapter on Canvas) (Lindsay)
  • Lecture: Bayesian modeling: in practice

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• Nov. 9
  • Presentation: SDM validation (Liz)
  • DUE: 1) Install the JAGS library; 2) Install the jagsUI R package; 3) run the JAGS example in Bayesian modeling: in practice; 4) add an additional climate covariate and refit the model.
  • Lecture: Bayesian modeling: in practice continued

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• Nov. 14
  • Lecture: Hierarchical time series models (Michael)

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• Nov. 16
  • DUE Ethics of forecasting: Start with Record et al., a blog post, and then read Hobday et al.
  • Lecture: Decadal forecast horizons and climate disequilibrium (Michael)

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• Nov. 21
  • Self-organize NEON forecast challenge (get everyone access to code)

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Nov. 23 NO CLASS (Thanksgiving)

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• Nov. 28
  • DUE: try to install Stan (rstan package) and run the example code in the lecture below
  • Lecture: Bayesian modeling: in practice
  • Lab: NEON forecast challenge

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• The rest of the schedule is still underconstruction!

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• Nov. 30
  • TBD

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• Dec. 5
  • TBD

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• December 7
  • Wrap-up discussion: Can we forecast in ecology (and what can we forecast)?
  • Reading: Prediction, precaution, and policy under global change
  • Discussion questions

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Archives:

• DUE:
  • Explore flu data and R package
  • Explore Bayesian time series analysis in R with bayesforecast, example code
  • Review flu forecasting literature

• Lab: Start working on flu forecast!

• OLD LINKS
  • Population forecasts – animals
  • Population forecasts – plants
  • Biodiversity forecasting
  • Epidemiological forecasts, like State-space modeling to support management of brucellosis in the Yellowstone bison population
  • Carbon cycle
  • Scenario Planning: a Tool for Conservation in an Uncertain World
  • Use of remote-sensing in forecasting
  • Validation of species-distribution models? Maguire et al. ; Blois et al.