https://github.com/robjhyndman/isi_workshop_2019

Materials for 1-day workshop on high dimensional time series analysis at ISI 2019
https://github.com/robjhyndman/isi_workshop_2019

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Materials for 1-day workshop on high dimensional time series analysis at ISI 2019

• Host: GitHub
• URL: https://github.com/robjhyndman/isi_workshop_2019
• Owner: robjhyndman
• Created: 2019-07-22T06:53:20.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-08-17T04:01:04.000Z (almost 6 years ago)
• Last Synced: 2025-01-30T00:22:14.975Z (5 months ago)
• Language: TeX
• Size: 25.7 MB
• Stars: 9
• Watchers: 2
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd

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README

        
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output: github_document

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```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>"

)

```

# ISI Workshop 2019

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