robjhyndman/tsfeatures: papers linking to it

GitHub · 264 stars · 43 forks · R · created 2017 · github.com/robjhyndman/tsfeatures

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to robjhyndman/tsfeatures (https://github.com/robjhyndman/tsfeatures), 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 robjhyndman/tsfeatures in their Crossref records, the first in 2019; most of them (1) in 2026.
By category: AI (2) and Economics (1); the journals with the most are Mineral Economics (1), Machine Learning (1) and Data Mining and Knowledge Discovery (1).
2 of the papers build on one another: a later paper cites an earlier one that also links robjhyndman/tsfeatures.

By year#

20192026

Papers linking to robjhyndman/tsfeatures by publication year
Year	Papers
2019	1
2025	1
2026	1

Journals#

Journals whose papers link to robjhyndman/tsfeatures most
Journal	Papers
Mineral Economics	1
Machine Learning	1
Data Mining and Knowledge Discovery	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

catch22: CAnonical Time-series CHaracteristics Data Mining and Knowledge Discovery, 2019
- Mast: interpretable stress testing via meta-learning for forecasting model robustness evaluation Machine Learning, 2025 — cites the paper above

Papers linking to robjhyndman/tsfeatures, most cited first
Published	Title	Journal	Authors	Citations	Link
2019-08-09	catch22: CAnonical Time-series CHaracteristics	Data Mining and Knowledge Discovery	Carl H Lubba, Sarab S. Sethi, Philip Knaute et al.	442	cites it
2025-10-15	Mast: interpretable stress testing via meta-learning for forecasting model robustness evaluation	Machine Learning	Ricardo Inácio, Vítor Cerqueira, Marília Barandas et al.	0	cites it
2026-10-05	Forecasting the volatility of energy transition metals	Mineral Economics	Andrea Bastianin, Xiao Li, Luqman Shamsudin	0	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). robjhyndman/tsfeatures: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-robjhyndman-tsfeatures

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

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