Dear all,
 
there are still a few seats available for our upcoming online course: Time 
Series Analysis and Forecasting in R.
 
Dates: 24–28 March 2025

Format: Online (to foster international participation)
Daily Schedule:
09:00–12:00 (Berlin time): Live lectures and practical introductions.
Self-guided exercises: Annotated R scripts for hands-on practice.
Course website: [ 
https://www.physalia-courses.org/courses-workshops/time-series-in-r/ ]( 
https://www.physalia-courses.org/courses-workshops/time-series-in-r/ )
 
Course Highlights:
Focus on Ecological Data: Learn to handle challenges like overdispersion, 
clustering, missing data, and nonlinear effects.
Cutting-edge Tools: Explore dynamic GLMs and GAMs using {mvgam} and {brms}, 
making Bayesian modelling in R.
Comprehensive Training: Gain skills in wrangling, visualizing, and analyzing 
time series data for insights and accurate forecasts.
Who Should Attend?
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.
Learning Outcomes:
1.    Understand how dynamic GLMs and GAMs work to capture both nonlinear 
covariate effects and temporal dependence2.    Be able to fit dynamic GLMs and 
GAMs in R using the {mvgam} and {brms} packages3.    Understand how to 
critique, visualize and compare fitted dynamic models4.    Know how to produce 
forecasts from dynamic models and evaluate their accuracies using probabilistic 
scoring rules
 
Best regards and Happy New Year,
Carlo


 
 

--------------------

Carlo Pecoraro, Ph.D


Physalia-courses DIRECTOR

i...@physalia-courses.org

[ www.physalia-courses.org ]( http://www.physalia-courses.org )



 
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