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M-competitions, from M4 to M5: reservations and expectations

2020-03-012024-03-15 3 Comments

UPDATE: I have also written a short post on “The role of M competitions in forecasting“, which gives historical perspective and a brief overview of the main findings of the previous competitions. Some of you might have noticed that the guidelines for the M5 competition have finally been released. Those of you who have previously […]

Naughty APEs and the quest for the holy grail

2017-07-292024-04-04 Leave a comment

Today I want to tell you a story of naughty APEs and the quest for the holy grail in forecasting. The topic has already been known for a while in academia, but is widely ignored by practitioners. APE stands for Absolute Percentage Error and is one of the simplest error measures, which is supposed to […]

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  • smooth forecasting with the smooth package in Python
  • The real Dunning-Kruger effect
  • There’s no such thing as “deterministic forecast”
  • Scaling of error measures
  • smooth v4.4.0

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ADAM AI and ML ARIMA bla-bla-bla CES changepoint combinations Competitions complex variables conferences consultancy English error measures estimators ETS extrapolation methods greybox GUM hierarchies history Information criteria intermittent demand ISF ISMS model combination model selection multivariate models papers personal PhD presentations programming Python R regression regular demand Seasonality SMA smooth statistics stories teaching theory time series uncertainty

Comments

  • Ivan Svetunkov on Intermittent demand classifications: is that what you need?
  • Kishor Kukreja on Intermittent demand classifications: is that what you need?
  • Ivan Svetunkov on Who is “Forecasting academia”?
  • Mohamed Merabtine on Who is “Forecasting academia”?
  • Ivan Svetunkov on Why Naive is not a good benchmark for intermittent demand

RSS RBloggers feed

  • Transgender Day of Visibility 2026-03-31
  • AGENTS.md, {admiral}, and the AI-Assisted Programmer 2026-03-31
  • rOpenSci News Digest, March 2026 2026-03-30
  • Better Git diff with difftastic 2026-03-30
  • Same model, better shape: why centering improves MCMC 2026-03-30

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Ivan Svetunkov
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