ETC3550/5550: Applied Forecasting

Reliable forecasts of business and economic variables must often be obtained against a backdrop of structural change in markets and the economy. This unit introduces methods suitable for forecasting in these circumstances including the decomposition of time series, exponential smoothing methods, ARIMA modelling, and regression with auto-correlated disturbances. Students can expect to enhance their computer skills with exercises using R.

Unit website

ETC4500/5450: Advanced R Programming

R is widely used as a tool for data analysis and one of the most popular programming languages. This unit delves into R from the programming aspect instead of using it as a data analysis tool. You will learn a variety of programming paradigms and delve into the foundations of R as a programming language.

Unit website

Tidy time series & forecasting in R

This is a 2-day workshop, run approximately once per year.


It is common for organizations to collect huge amounts of data over time, and existing time series analysis tools are not always suitable to handle the scale, frequency and structure of the data collected. In this workshop, we will look at some packages and methods that have been developed to handle the analysis of large collections of time series.

Day 1: We look at the tsibble data structure for flexibly managing collections of related time series. We look at how to do data wrangling, data visualizations and exploratory data analysis. We explore feature-based methods to explore time series data in high dimensions. A similar feature-based approach can be used to identify anomalous time series within a collection of time series, or to cluster or classify time series. Primary packages for day 1 are tsibble, lubridate and feasts (along with the tidyverse of course).

Day 2 is about forecasting. We look at some classical time series models and how they are automated in the fable package, and we will explore the creation of ensemble forecasts and hybrid forecasts. Best practices for evaluating forecast accuracy will also be covered. Finally, we will look at forecast reconciliation, allowing millions of time series to be forecast in a relatively short time while accounting for constraints on how the series are related.


This course is for you if you:

  • already use the tidyverse packages in R such as dplyr, tidyr, tibble and ggplot2;
  • need to analyze large collections of related time series; and
  • would like to learn how to use some tidy tools for time series analysis including visualization, decomposition and forecasting.

Selected previous workshops