tsfeatures (R package): papers linking to it

CRAN · CRAN.R-project.org/package=tsfeatures

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to tsfeatures (R package) (https://CRAN.R-project.org/package=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#

6 papers in the tracked journals link to tsfeatures (R package) in their Crossref records, the first in 2019; most of them (2) in 2022.
By category: AI (3), Operations Research (2) and Economics (1); the journals with the most are Data Mining and Knowledge Discovery (3), European Journal of Operational Research (2) and International Journal of Forecasting (1).
6 of the papers build on one another: a later paper cites an earlier one that also links tsfeatures (R package).

By year#

20192022

Papers linking to tsfeatures (R package) by publication year
Year	Papers
2019	2
2020	1
2021	1
2022	2

Journals#

Journals whose papers link to tsfeatures (R package) most
Journal	Papers
Data Mining and Knowledge Discovery	3
European Journal of Operational Research	2
International Journal of Forecasting	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
- Forecast evaluation for data scientists: common pitfalls and best practices Data Mining and Knowledge Discovery, 2022 — cites the paper above
FFORMA: Feature-based forecast model averaging International Journal of Forecasting, 2019
- Retail sales forecasting with meta-learning European Journal of Operational Research, 2020 — cites the paper above
  - Forecast with forecasts: Diversity matters European Journal of Operational Research, 2021 — cites the paper above
- Novel features for time series analysis: a complex networks approach Data Mining and Knowledge Discovery, 2022 — cites the paper above

Papers linking to tsfeatures (R package), 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
2019-09-27	FFORMA: Feature-based forecast model averaging	International Journal of Forecasting	Pablo Montero‐Manso, George A. Athanasopoulos, Rob J. Hyndman et al.	313	cites it
2022-12-02	Forecast evaluation for data scientists: common pitfalls and best practices	Data Mining and Knowledge Discovery	Hansika Hewamalage, Klaus Ackermann, Christoph Bergmeir	296	cites it
2020-05-28	Retail sales forecasting with meta-learning	European Journal of Operational Research	Shaohui Ma, Robert A. Fildes	180	cites it
2022-03-31	Novel features for time series analysis: a complex networks approach	Data Mining and Knowledge Discovery	Vanessa Freitas Silva, Maria Eduarda Silva, Pedro Ribeiro et al.	29	cites it
2021-10-19	Forecast with forecasts: Diversity matters	European Journal of Operational Research	Yanfei Kang, Wei Cao, Fotios Petropoulos et al.	5	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). tsfeatures (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-tsfeatures

@misc{scholar4ai-code-cran-tsfeatures,
  author = {{Scholar4AI}},
  title = {tsfeatures (R package): papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/cran-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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