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Event Tag: Statistics

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Events

  • Demand Forecasting Principles Training, November 2026 on 2026-11-03 14:00

Blog

  • smooth in python: Multiple Seasonal ARIMA
  • Demand Forecasting Principles training moved to November
  • Another important Naïve method
  • smooth in python: Multistep losses
  • Why Naïve is popular and important

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 Loss functions 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

RSS feed: RBloggers feed RBloggers feed

  • How to Create a Function in R: Custom Median & ggplot2 Examples 2026-09-09
  • Navigating Challenges in Spatial Machine Learning 2026-09-08
  • ahead (Time Series Forecasting with uncertainty quantification) gets a lot faster to install: most dependencies are now optional 2026-09-08
  • Research Workflows 2026-09-07
  • Visualising High-dimensional Data with R workshop 2026-09-05
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