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Course: Time Series Analysis using regression techniques

Highland Statistics Ltd 
Hello,


We would like to announce the following online stats course:*Time Series Analysis using regression techniques.*

This course also served as a good introduction to GAM and GAMM using mgcv.


Format: Online course with on-demand video and live Zoom sessions
When: Live summary sessions using Zoom will run on 17, 19, 24, 26, 28 October 2022.
Price: 500 GBP (50% reduction for developing countries).

Included: A 1-hour face-to-face video chat with one or both instructors.
Access to course material: 12 months

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





*Detailed outline*
A detailed outline of the course is provided below. All exercises consist of a data set, a video describing the data and the questions, R solution code, and a video discussing the R solution file. Preparation material on data exploration and two exercises are provided.


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