24-28 March 2025
To foster international participation, this course will be held online
Time series analysis and forecasting are standard goals in applied ecology. But most time series courses focus only on traditional forecasting models such as ARIMA or Exponential Smoothing. These models cannot handle features that dominate ecological data, including overdispersion, clustering, missingness, discreteness and nonlinear effects. Using the flexible and powerful Bayesian modelling software Stan, we can now meet this complexity head on. Packages such as {mvgam} and {brms} can build Stan code to specify ecologically appropriate models that include nonlinear effects, random effects and dynamic processes, all with simple interfaces that are familiar to most R users. In this course you will learn how to wrangle, visualize and explore ecological time series. You will also learn to use the {mvgam} and {brms} packages to analyse a diversity of ecological time series to gain useful insights and produce accurate forecasts. All course materials (presentations, practical exercises, data files, and commented R scripts) will be provided electronically to participants.
This course is aimed at higher degree research students and early career researchers working with time series data in the natural sciences (with particular emphasis on ecology) who want to extend
their knowledge by learning how to add dynamic processes to model temporal autocorrelation. Participants should ideally have some knowledge of regression including linear models, generalized
linear models and hierarchical (random) effects. But we’ll briefly recap these as we connect them to time series modelling.
Participants should be familiar with RStudio and have some fluency in programming R code. This includes an ability to import, manipulate (e.g. modify variables) and visualise data. There will be
a mix of lectures and hands-on practical exercises throughout the course.
1. Understand how dynamic GLMs and GAMs work to capture both nonlinear covariate effects and temporal dependence
2. Be able to fit dynamic GLMs and GAMs in R using the {mvgam} and {brms} packages
3. Understand how to critique, visualize and compare fitted dynamic models
4. Know how to produce forecasts from dynamic models and evaluate their accuracies using probabilistic scoring rules
09:00 - 12:00 (Berlin time): live lectures and introduction to / review of the practicals
3 additional hours: self-guided practicals using annotated R scripts
Monday– Classes from 9 AM-12 PM Berlin time
Introduction to time series and time series visualization
Some traditional time series models and their assumptions
GLMs and GAMs for ecological modelling
Temporal random effects and temporal residual correlation structures
Tuesday– Classes from 9 AM-12 PM Berlin time
Dynamic GLMs and Dynamic GAMs
Autoregressive dynamic processes
Gaussian Processes
Dynamic coefficient models
Wednesday– Classes from 9 AM-12 PM Berlin time
Forecasting from dynamic models
Point-based forecast evaluation
Probabilistic forecast evaluation
Bayesian posterior predictive checks
Thursday– Classes from 9 AM-12 PM Berlin time
Multivariate ecological time series
Vector autoregressive processes
Dynamic factor models
Multivariate forecast evaluation
Friday– Classes from 9 AM-12 PM Berlin time
Extended practical examples using {mvgam}
Review and open discussion
Should you have any further questions, please send an email to info@physalia-courses.org
Cancellation Policy:
> 30 days before the start date = 30% cancellation fee
< 30 days before the start date= No Refund.
Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.