Open Forecasting

Menu

Skip to content
  • Home
  • Events
    • Future events
    • Past events
  • smooth
    • smooth Github
    • smooth in Python
    • smooth in R
      • About adam() function
      • About es() function
      • Common parameters
      • smooth methods in R
  • greybox
    • greybox Github
    • greybox in Python
      • greybox on PyPI
    • greybox in R
  • Theory
    • Forecast evaluation
  • ADAM
  • SBA
  • About
  • Русский

combinations

ITISE2025: Beyond summary performance metrics for forecast selection and combination

2025-07-21 Leave a comment
A gist of pAIC

This year, I couldn’t attend the International Symposium on Forecasting (organised by the International Institute of Forecasters), which I usually do, so instead I went to Gran Canaria for the International Conference on Time Series and Forecasting (aka ITISE). The location was fantastic, and I enjoyed several talks. I was also glad to catch up […]

A paper to read over the Xmas holiday: Wang et al. (2023) – Forecast combinations: An over 50-year review

2024-12-232024-12-23 Leave a comment

Christmas and the New Year are upon us, and I wanted to publish a celebratory post before taking a break. Instead of writing something educational, I decided to simply recommend a paper for you to read over the holidays – something you might have overlooked in the past couple of years. Here it is or […]

  • Русский

Events

  • Gathering the Pool: Model Performance Based Approach for Forecast Combinations on 2026-03-13 12:00

Blog

  • There’s no such thing as “deterministic forecast”
  • Scaling of error measures
  • smooth v4.4.0
  • Forecasting Competitions Datasets in Python
  • Risky business: how to select your model based on risk preferences

Tags

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

  • MBBEFDLite has its mature release 2026-03-11
  • Sharing data across shiny modules, an update 2026-03-10
  • Breaking Release of the patentsview R Package 2026-03-10
  • Formula 1 Analysis in R with f1dataR: Lap Times, Pit Stops, and Driver Performance 2026-03-09
  • Get Better: loading multiple csv files in R 2026-03-09

Categories

Blog Authors

Ivan Svetunkov
  • Home
  • Future events
  • Past events
  • Using R
  • About
Рейтинг@Mail.ru