Scholar4AI Updated 2026-11-03 14:43 UTC+09:00

forecast (R package): papers linking to it

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

← Code and data repositories cited in research papers

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

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Key findings#

  • 19 papers in the tracked journals link to forecast (R package) in their Crossref records, the first in 2017; most of them (4) in 2025.
  • By category: Economics (6), AI (4) and Finance (3); the journals with the most are International Journal of Forecasting (2), Letters in Spatial and Resource Sciences (2) and Journal of Marketing Analytics (2).
  • 5 of the papers build on one another: a later paper cites an earlier one that also links forecast (R package).

By year#

2017: 2 papers2018: 3 papers2019: 2 papers2020: 2 papers2021: 1 paper2022: 1 paper2023: 2 papers2024: 1 paper2025: 4 papers2026: 1 paper
20172026
Papers linking to forecast (R package) by publication year
YearPapers
20172
20183
20192
20202
20211
20221
20232
20241
20254
20261

Journals#

Journals whose papers link to forecast (R package) most
JournalPapers
International Journal of Forecasting2
Letters in Spatial and Resource Sciences2
Journal of Marketing Analytics2
Expert Systems with Applications2
Journal of Empirical Finance1

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.

Papers linking to forecast (R package), most cited first
PublishedTitleJournalAuthorsCitationsLink
2020-07-06Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithmsNeural Computing and ApplicationsHristos Tyralis, Georgia Papacharalampous, Andreas Langousis177cites it
2017-01-11Visualising forecasting algorithm performance using time series instance spacesInternational Journal of ForecastingYanfei Kang, Rob J. Hyndman, Kate Smith‐Miles157cites it
2020-08-25Implementing transfer learning across different datasets for time series forecastingPattern RecognitionRui Ye, Qun Dai129cites it
2019-04-17A simple combination of univariate modelsInternational Journal of ForecastingFotios Petropoulos, Ivan Svetunkov80cites it
2018-01-02Forecasting global stock market implied volatility indicesJournal of Empirical FinanceStavros Antonios Degiannakis, George N. Filis, Hossein Hassani58cites it
2022-01-19Vaccination strategies and transmission of COVID-19: Evidence across advanced countriesJournal of Health EconomicsDongwoo Kim, Young Jun Lee43cites it
2017-02-22Heuristic decision rules for short-term trading of renewable energy with co-located energy storageComputers & Operations ResearchMichael Hassler32cites it
2018-06-18Rare earth elements price forecasting by means of transgenic time series developed with ARIMA modelsResources PolicyMaría Victoria Riesgo García, Alicja Krzemień, Miguel Ángel Manzanedo del Campo et al.31cites it
2021-09-18Measuring systemic risk and contagion in the European financial networkEmpirical EconomicsLaleh Tafakori, Armin Pourkhanali, Riccardo Rastelli25cites it
2018-12-07Intraday forecasts of a volatility index: functional time series methods with dynamic updatingAnnals of Operations ResearchHan Lin Shang, Yang Yang, Fearghal Kearney22cites it
2023-09-18An algorithm for automatic selection and combination of forecast modelsExpert Systems with ApplicationsCarlos García-Aroca, María Asunción Martínez Mayoral, Javier Morales et al.8cites it
2019-03-12Seasonal time series forecasting by the Walsh-transformation based techniqueCentral European Journal of Operations ResearchErik B. Bajalinov, Szabolcs Duleba5cites it
2024-05-06The future of regional inequalities: an ARIMA forecastLetters in Spatial and Resource SciencesHasan Engìn Duran5cites it
2025-09-23Marketing predictions under macroeconomic volatility: empirical evidence for automotive SMEs from a machine learning perspectiveJournal of Marketing AnalyticsManuel Muth, Anita Talitha Parsegyan, Julian Litzinger et al.3cites it
2025-08-22Spindle thermal error modeling with linear and nonlinear hybrid tandem metamodel based on statistical and machine learning approachesExpert Systems with ApplicationsMenghua Li, Mingchang Chih, Chia-Fu Kou et al.2cites it
2023-07-11Primary market demand for German government bondsJournal of International Money and FinanceJakob Shida1cites it
2025-06-04The future of urban hierearchy and Zipf law: ARIMA and BATS forecastingLetters in Spatial and Resource SciencesHasan Engìn Duran1cites it
2026-06-13Market analytics beyond big data: pretrained AI models for industrial order forecasting in GermanyJournal of Marketing AnalyticsManuel Muth, Jonas Engel, Gerd Nufer1cites it
2025-10-27Threshold Vector Error Correction for Modeling the Changes in Gasoline Price with Data ImputationAsia-Pacific Financial MarketsRyan S. Gill, Rasitha R. Jayasekare, Kiseop Lee0cites 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). forecast (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-forecast

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

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