openforecast-org/smooth: papers linking to it

GitHub · 107 stars · 23 forks · Python · license LGPL-2.1 · created 2016 · github.com/openforecast-org/smooth

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to openforecast-org/smooth (https://github.com/openforecast-org/smooth), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

3 papers in the tracked journals link to the GitHub repository openforecast-org/smooth in their Crossref records, the first in 2019; most of them (1) in 2023.
By category: Economics (1), Operations Research (1) and Statistics (1); the journals with the most are International Journal of Forecasting (1), European Journal of Operational Research (1) and Computational Statistics (1).

By year#

20192023

Papers linking to openforecast-org/smooth by publication year
Year	Papers
2019	1
2022	1
2023	1

Journals#

Journals whose papers link to openforecast-org/smooth most
Journal	Papers
International Journal of Forecasting	1
European Journal of Operational Research	1
Computational Statistics	1

Papers linking to openforecast-org/smooth, most cited first
Published	Title	Journal	Authors	Citations	Link
2019-04-17	A simple combination of univariate models	International Journal of Forecasting	Fotios Petropoulos, Ivan Svetunkov	80	cites it
2022-05-04	A new taxonomy for vector exponential smoothing and its application to seasonal time series	European Journal of Operational Research	Ivan Svetunkov, Huijing Chen, John E. Boylan	32	cites it
2023-06-24	Multi-step estimators and shrinkage effect in time series models	Computational Statistics	Ivan Svetunkov, Nikolaos Kourentzes, Rebecca Killick	7	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). openforecast-org/smooth: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-openforecast-org-smooth

@misc{scholar4ai-code-github-openforecast-org-smooth,
  author = {{Scholar4AI}},
  title = {openforecast-org/smooth: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-openforecast-org-smooth}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).