- 9am-10am Monday (online)
- 4pm-5pm Thursday (online)
- 10am-11am Friday (online)
- Read Student FAQs
5 ECTS module corresponds to about 100-125 hours of study time. This includes the several hours of lectures (30+ for undersgrads, 20+ for postgrads). The academic year structure is dense and good time management is essential.
Contacting the lecturer
A Discussion Forum is/will be opened online (e.g. with Blackboard or/or Teams/Yammer provided to students in TCD) to post queries and is used as the primary tool for discussion on topics related to the course. Emails from students in my taught modules might be overlooked for logistic and time management reasons. If so, please ask questions during classes or flag this email to me directly at the end of the class.
Lecturenotes are available as a PDF file. Its table of content describes the syllabus. Additional materials may be shared on Blackboard and also on Github https://github.com/Roznn/Forecasting-with-R. Programming language and software used for this course are https://www.r-project.org/ with https://rstudio.com/products/rstudio/.
Keywords: time series, time series visualisation, Holt-Winters algorithms, ARIMA models, forecasting, prediction intervals
Last year module webpage STU33010 (MT2019)
This module will be assessed by a combination of various continuous assessments including quizzes, in-class test, and projects.
There are a lot of books on time series, forecasting in the Library that are relevant to the course. Below the first book is given as an example reference. It is available in the library and most of the time series used in the labs are explained in that book.
The https://t.co/S3BpRgtxUW paper with its finding that the worse Stat forecasting method was more accurate than the best of the ML ones has passed the 100,000 mark of views/downloads. None of those who have read/downloaded it has challenged its finding. We are still waiting! pic.twitter.com/3Y7iC51CXm— Spyros Makridakis (@spyrosmakrid) September 13, 2019
Slides and code from my #rstatsnyc talk on "Forecasting ensembles using fable" now available at https://t.co/wB1GhYXqE0. #rstats #forecasting Thanks to @mitchoharawild for the packages. pic.twitter.com/GTpKLQxz0Y— Rob J Hyndman (@robjhyndman) August 14, 2020