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Time series analysis & forecasting using R |
Table of contents |
ANU, Canberra |
Room 5.02, Marie Reay Teaching Building, The Australian National University in Canberra.
9-10 November 2022
It is becoming increasingly 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.
On day 1, we will look at the tsibble data structure for flexibly managing collections of related time series. We will look at how to do data wrangling, data visualizations and exploratory data analysis. We will 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 will be tsibble, lubridate and feasts (along with the tidyverse of course).
Day 2 will be about forecasting. We will look at some classical time series models and how they are automated in the fable package. We will look at creating ensemble forecasts and hybrid forecasts, as well as some new forecasting methods that have performed well in large-scale forecasting competitions. 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.
Attendees will learn:
- How to wrangle time series data with familiar tidy tools.
- How to compute time series features and visualize large collections of time series.
- How to select a good forecasting algorithm for your time series.
- How to ensure forecasts of a large collection of time series are coherent.
This course will be appropriate for you if you answer yes to these questions:
- Do you already use R regularly, especially the tidyverse packages, or are willing to do suitable pre-course training in R and tidyverse?
- Do you need to analyse large collections of related time series?
- Would you like to learn how to use some new tidy tools for time series analysis including visualization, decomposition and forecasting?
Participants should be familiar with the use of R, at least to the point where they can fit a linear regression model, and work with data frames.
People who don't use R regularly, or don't know the tidyverse packages, are recommended to do the tutorials at learnr.numbat.space beforehand.
Please bring your own laptop with a recent version of R and RStudio installed. The following code will install the main packages needed for the workshop.
install.packages(c("tidyverse","fpp3","GGally", "sugrrants"))Time Activity
09:00 - 10:30 Session 1 10:30 - 11:00 Coffee break 11:00 - 12:30 Session 2 12:30 - 13:30 Lunch break 13:30 - 15:00 Session 3 15:00 - 15:30 Coffee break 15:30 - 17:00 Session 4