Useful extensions for online books

I’ve had two recent questions from readers of my online textbook (with George Athanasopoulos) which could be solved using Google Chrome extensions. Hi, I’m an MSc student and am shortly starting my project/dissertation on time series data. I’ve started reading Version 3 of your book and improving my R skills but am wondering if there’s any way I can read V3 that will allow annotation? Thanks For personal annotation of websites, the Hypothesis extension is very useful.

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What is forecasting?

Time series cross-validation using fable

Time series cross-validation is handled in the fable package using the stretch_tsibble() function to generate the data folds. In this post I will give two examples of how to use it, one without covariates and one with covariates. Quarterly Australian beer production Here is a simple example using quarterly Australian beer production from 1956 Q1 to 2010 Q2. First we create a data object containing many training sets starting with 3 years (12 observations), and adding one quarter at a time until all data are included.

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Recent papers

  • Kasun Bandara, Rob J Hyndman, Christoph Bergmeir (2021) MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns. Abstract  pdf
  • Evangelos Spiliotis, Mahdi Abolghasemi, Rob J Hyndman, Fotios Petropoulos, Vassilios Assimakopoulos (2021) Hierarchical forecast reconciliation with machine learning. Applied Soft Computing, to appear. Abstract  pdf
  • Atefeh Zamani, Hossein Haghbin, Maryam Hashemi, Rob J Hyndman (2021) Seasonal functional autoregressive models. J Time Series Analysis, to appear. Abstract DOI  pdf
  • Claire Kermorvant, Benoit Liquet, Guy Litt, Kerrie Mengersen, Erin E Peterson, Rob J Hyndman, Jeremy B Jones Jr, Catherine Leigh (2021) Understanding links between water-quality variables and nitrate concentration in freshwater streams using high-frequency sensor data. Abstract  pdf
  • Alex Dokumentov and Rob J Hyndman (2021) STR: Seasonal-Trend decomposition using Regression. INFORMS Journal on Data Science, to appear. Abstract  pdf  code

Recent and upcoming seminars

  • Probabilistic ensemble forecasting of Australian COVID-19 cases. (29 June 2021) YouTube More info...
  • Forecasting elements that stand the test of time. (6 May 2021) YouTube More info...
  • Seriously social podcast. (12 April 2021) More info...
  • Developing good research habits. (23 March 2021) More info...
  • Forecasting impact podcast. (6 February 2021) More info...