Creating social good for forecasters

Rob J Hyndman

Social good for forecasters

  • Data sets
  • Software
  • Textbooks
  • Education resources
  • Research papers

Social good is created when we make these resources widely and freely available.

Data sets

Large, open-access data sets:

  • help forecasters test new methods
  • allow easier empirical comparisons
  • help generate interesting student exercises

Time series data library (1996)

Time series data library (2012)

Time series data library (2022)

Other R packages with data

fma:

Data from Forecasting: methods & applications (1998)

expsmooth:

Data from Forecasting with exponential smoothing: the state space approach (2008)

fpp:

Data from Forecasting: principles & practice (2013)

fpp2:

Data from Forecasting: principles & practice (2nd ed, 2018)

fpp3:

Data from Forecasting: principles & practice (3rd ed, 2021)

Mcomp:

Data from M and M3 competitions, with contributed forecasts

tscompdata:

Data from M, M3, NN3, NN5, NNGC1, tourism and GEFCom2012 competitions

forecastingdata.org

Software

Open source software:

  • makes state-of-the-art forecasting methods available to everyone
  • ensures new forecasting methods can be easily used
  • influences the way people think about forecasting

Open source R packages

≤ 2002

Collection of R functions used for consulting projects, available at robjhyndman.com

2003

ets, thetaf. forecast v0.xx available at robjhyndman.com

2004
2005
2006

forecast v1.0 on CRAN

2007

auto.arima

2008

JSS paper (Hyndman & Khandakar)

2009

forecast v2.0 unbundled

2010

arfima

2011

tslm, stlf, naive, snaive, tbats. forecast v3.0 with Box Cox transformations

2012

nnetar. Package moved to github. forecast v4.0

2013

Major speed-up of ets

2014

tsoutliers, tsclean. forecast v5.0

2015

New plots. forecast v6.0

2016

ggplot2 graphics, bias adjustment, forecast v7.0

2017

forecast v8.0

2018

tsibble released on CRAN

2019

fable and feasts released on CRAN

2020
2021

FPP3 textbook using fable published

2022

forecast v8.16, fable v0.3.1, feasts v0.2.2 on CRAN

R package downloads

CRAN Task View: Time series analysis

Textbooks

Open-access textbooks:

  • makes high quality forecasting materials available to everyone
  • ensures new forecasting methods can be easily used
  • influences the way people think about forecasting

My first book (1998)

Online publishing: FPP0

Online publishing: FPP1

Online publishing: OTexts.com/fpp2

Online publishing: OTexts.com/fpp3

FPP monthly readers

In past year:

  • 27K page views per day
  • 3K new users per day

To date:

  • 40.6 million page views
  • 5.4 million users
  • from 209 countries

FPP translations

Translations:

  • FPP2 available in Chinese and Korean
  • FPP3 available in Japanese, Italian, Greek
  • FPP3 translations underway in French, Spanish, Russian, Portuguese

Online publishing: in print

Education resources

Open-access education resources

  • Improves forecasting education everywhere
  • Equips non-experts to teach forecasting

FPP exercise solutions

Requested by 1183 instructors from 91 countries

Slides for a forecasting course

Research papers

Open-access research papers

  • Makes latest research available to everyone
  • Research ideas available earlier
  • Allows for informal peer-review

Open-access research papers

  • Bypass the journal pay-walls
  • Every journal allows pre-prints to continue to exist online after a paper is accepted
  • It reduces the risk of someone beating you – there is a public paper with a date on it.
  • Most government funding agencies now require pre-prints to be made available.
  • It increases your citations
  • Many online repositories available: arXiv, RePEc, SSRN, …

NEP-FOR: weekly report

Questions

 

  • Why do I do this?
  • What could you contribute?

Why do I do this?

  • I want to do things that are useful and have impact.
  • Most of this activity increases citations of my papers.
  • I get a lot of consulting requests and speaking invitations because people know me from my open source work.

What could you contribute?

  • Add data to forecastingdata.org
  • Good open source python packages
  • Other online textbooks
  • Forecasting educational resources: videos, activities, exercises, slides, …
  • Put all your research papers on arXiv or RePEc
  • Use reproducible practices and put your papers and code in public github repositories.

For more information

Slides and links: robjhyndman.com/seminars/f4sg2022