Tidy Time Series Analysis and Forecasting Workshop
9:00 AM-5:00 PM
2 Day Workshop
Professor of Statistics
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 new 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 feast (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.
This course will be appropriate for you if you answer yes to these questions:
- Do you already use the tidyverse packages in R such as dplyr, tidyr, tibble and ggplot2?
- 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?