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Event Tag: R

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Demand Forecasting Principles Training, October 2026 on 2026-10-05 14:00

Blog

ISF2026: PTS Taxonomy of Multiple Source of Error State Space Models for Demand Forecasting
stick function for the EDA in time series
smooth in python: Non-normal distributions in ETS/ARIMA
Hans Levenbach’s classification scheme for trend/seasonal components
smooth in python: multiple seasonal ETS

Tags

ADAM AI and ML ARIMA CES changepoint combinations Competitions complex variables conferences consultancy EDA error measures estimators ETS extrapolation methods greybox GUM history Information criteria intermittent demand ISF Marketing stuff model combination model selection multivariate models MUSE opinion 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

Announcing AI in Production 2027 2026-08-12
From Raw Data to Regulatory Results: Clinical Trial Programming in R using the pharmaverse workshop 2026-08-12
The Resource Project: Tell R How Much Memory You Need 2026-08-11
R-Community A moderated Google Group to share R knowledge 2026-08-10
Dual scaled y-axis with ggplot() 2026-08-10

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