Hyndsight

Thoughts on research, forecasting, statistics, and other distractions.

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All Hyndsight posts by date

Useful extensions for online books

18 June 2021

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.

What is forecasting?

3 June 2021

forecasting

Time series cross-validation using fable

16 May 2021

time series, forecasting, data science

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.

Forecasting podcasts

14 April 2021

forecasting, podcast

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.

(Updated: 30 May 2021)

Date	Podcast	Episode
24 May 2021	Data Skeptic	Forecasting principles and practice
12 April 2021	Seriously Social	Forecasting the future: the science of prediction
6 February 2021	Forecasting Impact	Rob Hyndman
19 July 2020	The Curious Quant	Forecasting COVID, time series, and why causality doesnt matter as much as you think‪
27 May 2020	The Random Sample	Forecasting the future & the future of forecasting
9 October 2019	Thought Capital	Forecasts are always wrong (but we need them anyway)

Call for papers: Innovations in hierarchical forecasting

26 October 2020

research, ijf, forecasting

There is a new call for papers for a special issue of the International Journal of Forecasting on “Innovations in hierarchical forecasting”.

Guest editors: George Athanasopoulos, Rob J Hyndman, Anastasios Panagiotelis, and Nikolaos Kourentzes.

Submission deadline: 31 August 2021.

Co-authorships for sale

20 October 2020

research, publishing, journals, writing

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.

Contraceptive forecasting competition

24 July 2020

time series, forecasting

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.

Terminology matters

26 June 2020

time series, statistics, econometrics

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.

Seasonal mortality rates

24 May 2020

time series, statistics, demography

The weekly mortality data recently published by the Human Mortality Database can be used to explore seasonality in mortality rates. Mortality rates are known to be seasonal due to temperatures and other weather-related effects (Healy 2003).

Excess deaths for 2020

21 May 2020

time series, statistics, epidemiology, demography

The reported COVID19 deaths in each country are often undercounts due to different reporting practices, or people dying of COVID19 related causes without ever being tested. One way to explore the true mortality effect of the pandemic is to look at “excess deaths” — the difference between death rates this year and the same time in previous years. The Financial Times (and other media outlets) have been collecting data from many countries to try to measure this effect.