Time series and forecasting workshop: 9-10 November 2022
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 analyse 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.
Places are limited, so sign up early if you’re interested.