[R-sig-eco] Course: Time series analysis using regression techniques

[Highland Statistics Ltd]

We would like to announce the following online stats course:

Course: Time series analysis using regression techniques


Format: Online course with on-demand video and live Zoom sessions
When: Live summary sessions using Zoom will run in October 2022.
Price: 500 GBP (50% reduction for developing countries).
Included: A 1-hour face-to-face video chat with one or both instructors.

Flyer: 
http://highstat.com/Courses/Flyers/2022/Flyer2022_10_TimeSeries_Online.pdf
Website: http://highstat.com/index.php/courses-upcoming

A detailed outline of the course is provided below. All exercises 
contain a video discussing the R solution code. Revision material on 
data exploration and multiple linear regression is provided. All theory 
material is also presented in videos.


Module 1
Revision exercise on multiple linear regression.
Short theory presentation on matrix notation.
Theory presentation 'Introduction to GAM'.
Three exercises to get familiar with GAM

Module 2
Theory presentation: How to include auto-regressive correlation in a 
regression model.
Exercise showing how to fit a GLM with AR1 correlation in glmmTMB.
Exercise on GAM with auto-regressive correlation applied to a regular 
spaced time-series data set.
Exercise on GAM with auto-regressive correlation applied to an irregular 
spaced time-series data set.
Exercise on detecting important changes in trends.

Module 3
Theory presentation on linear mixed-effects models.
Exercise on linear mixed-effects models.
Three exercises on the application of GAMM on time-series data sets.

Module 4
Theory presentation on distributions.
Theory presentation: Revision of Poisson and negative binomial GLM.
Revision exercise on Poisson and negative binomial GLM.
Exercise on Poisson and negative binomial GLMM with auto-regressive 
correlation
applied to a time-series data set.
Exercise on Poisson and negative binomial GAM applied to a time-series 
data set.

Module 5
Exercise on Bernoulli GAMM applied to time-series data set.
Exercise on beta GAMM applied to a time-series data set.
Exercise on binomial GAM(M) applied to a time-series data set.
Exercise on gamma GAM(M) applied to a time-series data set.
Exercise on Tweedie GAM(M) applied to a time-series data set.


Kind regards,

Alain Zuur