The tsoutliers() function in the forecast package for R is useful for identifying anomalies in a time series. However, it is not properly documented anywhere. This post is intended to fill that gap.
The function began as an answer on CrossValidated and was later added to the forecast package because I thought it might be useful to other people. It has since been updated and made more reliable.
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.
I’ve been interviewed for several podcasts over the last year or so. It’s always fun to talk about my work, and I hope there is enough differences between them to make it interesting for listeners. Here is a full list of them.
This is an interesting development! How many papers are published by bogus authors, and what is the going price for a coauthorship? Needless to say, this is appalling and contrary to every academic integrity policy I’ve seen. See the Monash authorship policy for example.
Dear Hyndman, Rob J.
Hope you are doing well.
I write this letter on behalf of authors seeking to co-publish. We have seen your previous works (https://www.
Here’s an interesting new forecasting competition that came via my inbox this week.
Contraceptive access is vital to safe motherhood, healthy families, and prosperous communities. Greater access to contraceptives enables couples and individuals to determine whether, when, and how often to have children. In low- and middle-income countries (LMIC) around the world, health systems are often unable to accurately predict the quantity of contraceptives necessary for each health service delivery site, in part due to insufficient data, limited staff capacity, and inadequate systems.
I was reminded again this week that getting the right terminology is important. Some of my colleagues who work in machine learning wrote a paper entitled “Time series regression” which began with “This paper introduces Time Series Regression (TSR): a little-studied task …”. Statisticians and econometricians have done time series regression for many decades, so this beginning led to the paper being lampooned on Twitter.
The problem arose due to clashes in terminology being used in different fields.