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