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Scaling of error measures
smooth v4.4.0
Forecasting Competitions Datasets in Python
Risky business: how to select your model based on risk preferences
Teaching Statistics and Descriptive Analytics in the world of AI

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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

RBloggers feed

CDCPLACES 1.2.0 2026-02-16
Machine Learning for Sports Analytics in R: A Complete Professional Guide 2026-02-15
Testing for interactions in nonlinear regression 2026-02-14
How Posit’s Public Package Manager manylinux_2_28 repository can help you if your R project is stuck on Ubuntu Focal Fossa 2026-02-12
Fitting time-to-event models with an environmental covariate 2026-02-12

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