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

fpp2 (R package): papers linking to it

CRAN · CRAN.R-project.org/package=fpp2

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to fpp2 (R package) (https://CRAN.R-project.org/package=fpp2), 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#

  • 25 papers in the tracked journals link to fpp2 (R package) in their Crossref records, the first in 2019; most of them (5) in 2026.
  • By category: AI (10), Operations Research (5) and Economics (4); the journals with the most are Applied Intelligence (3), Letters in Spatial and Resource Sciences (2) and Neural Computing and Applications (2).
  • 2 of the papers build on one another: a later paper cites an earlier one that also links fpp2 (R package).

By year#

2019: 1 paper2020: 2 papers2021: 3 papers2022: 2 papers2023: 3 papers2024: 4 papers2025: 5 papers2026: 5 papers
20192026
Papers linking to fpp2 (R package) by publication year
YearPapers
20191
20202
20213
20222
20233
20244
20255
20265

Journals#

Journals whose papers link to fpp2 (R package) most
JournalPapers
Applied Intelligence3
Letters in Spatial and Resource Sciences2
Neural Computing and Applications2
Computational Economics2
Computers & Industrial Engineering2

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 fpp2 (R package), most cited first
PublishedTitleJournalAuthorsCitationsLink
2022-12-02Forecast evaluation for data scientists: common pitfalls and best practicesData Mining and Knowledge DiscoveryHansika Hewamalage, Klaus Ackermann, Christoph Bergmeir296cites it
2020-02-22Demand forecasting in supply chain: The impact of demand volatility in the presence of promotionComputers & Industrial EngineeringMahdi Abolghasemi, Eric J. Beh, Garth Tarr et al.202cites it
2020-07-04Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approachNeural Computing and ApplicationsMohammed Mudassir, Shada Bennbaia, Devrim Ünal et al.182cites it
2021-08-14Inventory – forecasting: Mind the gapEuropean Journal of Operational ResearchThanos E. Goltsos, Aris A Syntetos, Christoph H. Glock et al.115cites it
2021-06-03Forecasting air passenger numbers with a GVAR modelAnnals of Tourism ResearchUlrich Gunter, Bozana Zekan47cites it
2023-01-23Export- and import-based economic models for predicting global trade using deep learningExpert Systems with ApplicationsCheng‐Hong Yang, Cheng-Feng Lee, Po-Yin Chang38cites it
2021-08-21Out-of-time cross-validation strategies for classification in the presence of dataset shiftApplied IntelligenceSebastián Maldonado, Julio López, Andrés Iturriaga24cites it
2019-07-26SAZED: parameter-free domain-agnostic season length estimation in time series dataData Mining and Knowledge DiscoveryMaximilian Toller, Tiago Santos, Roman Kern18cites it
2024-07-19Shaping closed-loop supply chain dynamics: Mitigating the bullwhip effect and improving customer satisfaction in production systems with material reuseComputers & Industrial EngineeringCarmen Liping Fernández-Arribas, Borja Ponte, Isabel Fernández17cites it
2025-05-07A Novel Hybrid Ensemble Framework for Stock Price Prediction: Combining Bagging, Boosting, Dagging, and StackingComputational EconomicsAqib Gul12cites it
2023-01-25Correlated product of experts for sparse Gaussian process regressionMachine LearningManuel Schürch, Dario Azzimonti, Alessio Benavoli et al.11cites it
2023-12-29Effectiveness of ATM withdrawal forecasting methods under different market conditionsTechnological Forecasting and Social ChangeMarcin Suder, Henryk Gurgul, Belém Barbosa et al.8cites it
2024-07-02Forecasting the waste production hierarchical time series with correlation structureOptimization and EngineeringIvan Eryganov, Martin Rosecký, Radovan Šomplák et al.7cites it
2026-02-01FinBERT-BiLSTM: a deep learning model for predicting volatile cryptocurrency market prices using market sentiment dynamicsApplied IntelligenceMabsur Fatin Bin Hossain, Lubna Zahan Lamia, Md Mahmudur Rahman et al.6cites it
2024-02-24Measuring regularity of human physical activities with entropy modelsJournal Of Big DataKeqin Shi, Zhen Chen, Weiqiang Sun et al.5cites it
2024-05-06The future of regional inequalities: an ARIMA forecastLetters in Spatial and Resource SciencesHasan Engìn Duran5cites it
2025-05-08MSR-GAN: multi-scales decomposition representations for unsupervised anomaly detectionApplied IntelligenceDongwei Xu, Tianhao Xia, Jiaye Hou et al.3cites it
2025-02-14Soybean futures responses to meteorological disaster risk —— Empirical evidence from the Chicago board of tradeFinance research lettersYu Yang, Huilu Rong, Gaoxin Cheng et al.1cites it
2025-06-04The future of urban hierearchy and Zipf law: ARIMA and BATS forecastingLetters in Spatial and Resource SciencesHasan Engìn Duran1cites it
2025-12-24A perceived quality quantification method for experiential products considering ambiguity, correlation, and dynamics of featuresElectronic Commerce ResearchQianqian Chen, zhongjun tang, Duokui He et al.1cites it
2022-02-22Optimal pricing for electricity retailers based on data-driven consumers’ price-responseTopRomán Pérez-Santalla, Miguel Carrión, Carlos Ruiz0cites it
2026-02-28Is the financial market efficient? An advanced time series analysisFinancial InnovationSiqi Mao, Lin-Xi Meng, Adrian Barbu et al.0cites it
2026-03-21WaveESN–RegimeMLP: GA-Tuned Reservoirs and Regime-Aware Multiscale ForecastingComputational EconomicsÇağlar Sözen0cites it
2026-04-21Evaluating LSTM deep learning models for agricultural price forecasting: a case study on citrus markets in Corrientes, ArgentinaNeural Computing and ApplicationsClaudia Screpnik, Eduardo Zamudio, Laura Giménez0cites it
2026-05-28Prediction of stable GARCH time series using auxiliary processesMetrikaMohammad Mohammadi, W. Brent Lindquist0cites 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). fpp2 (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-fpp2

@misc{scholar4ai-code-cran-fpp2,
  author = {{Scholar4AI}},
  title = {fpp2 (R package): papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/cran-fpp2}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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