High-dimensional time series analysis

17 August 2019

Presenters

Course description

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 and type of data collected. In this workshop, we will look at some new methods that have been developed to handle the analysis of large collections of time series.

We will explore feature-based visualizations and interactive visualizations, in order 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. Finally, we will discuss how fast automatic forecasting algorithms, along with sparse forecast reconciliation, can allow millions of time series to be forecast in a relatively short time

Syllabus

  1. Tidy time series analysis using tsibbles.
  2. Interactive visualization of high-dimensional time series.
  3. A feature-based approach to time series analysis
  4. Automatic forecasting algorithms
  5. Optimal forecast reconciliation

Prerequisites

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.

Please bring your own laptop with a recent version of R and RStudio installed


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