---
title: "forecast (R package): papers citing this CRAN package"
description: "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."
url: https://scholar4ai.com/code/cran-forecast
markdown_url: https://scholar4ai.com/code/cran-forecast.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# forecast (R package): papers linking to it

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.

- CRAN
- https://CRAN.R-project.org/package=forecast

All repositories: https://scholar4ai.com/code.md

## 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

*Papers linking to forecast (R package) by publication year*

| Year | Papers |
|---|---:|
| 2017 | 2 |
| 2018 | 3 |
| 2019 | 2 |
| 2020 | 2 |
| 2021 | 1 |
| 2022 | 1 |
| 2023 | 2 |
| 2024 | 1 |
| 2025 | 4 |
| 2026 | 1 |

## Journals

*Journals whose papers link to forecast (R package) most*

| Journal | Papers |
|---|---:|
| [International Journal of Forecasting](https://scholar4ai.com/journals/international-journal-of-forecasting.md) | 2 |
| [Letters in Spatial and Resource Sciences](https://scholar4ai.com/journals/letters-in-spatial-and-resource-sciences.md) | 2 |
| [Journal of Marketing Analytics](https://scholar4ai.com/journals/journal-of-marketing-analytics.md) | 2 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2 |
| [Journal of Empirical Finance](https://scholar4ai.com/journals/journal-of-empirical-finance.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [A simple combination of univariate models](https://doi.org/10.1016/j.ijforecast.2019.01.006) (International Journal of Forecasting, 2019)
  - [Marketing predictions under macroeconomic volatility: empirical evidence for automotive SMEs from a machine learning perspective](https://doi.org/10.1057/s41270-025-00436-0) (Journal of Marketing Analytics, 2025) — cites the paper above
    - [Market analytics beyond big data: pretrained AI models for industrial order forecasting in Germany](https://doi.org/10.1057/s41270-026-00489-9) (Journal of Marketing Analytics, 2026) — cites the paper above
- [The future of regional inequalities: an ARIMA forecast](https://doi.org/10.1007/s12076-024-00379-7) (Letters in Spatial and Resource Sciences, 2024)
  - [The future of urban hierearchy and Zipf law: ARIMA and BATS forecasting](https://doi.org/10.1007/s12076-025-00408-z) (Letters in Spatial and Resource Sciences, 2025) — cites the paper above

## Papers

*Papers linking to forecast (R package), most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2020-07-06 | [Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms](https://doi.org/10.1007/s00521-020-05172-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis | 177 | cites it |
| 2017-01-11 | [Visualising forecasting algorithm performance using time series instance spaces](https://doi.org/10.1016/j.ijforecast.2016.09.004) | [International Journal of Forecasting](https://scholar4ai.com/journals/international-journal-of-forecasting.md) | Yanfei Kang, Rob J. Hyndman, Kate Smith‐Miles | 157 | cites it |
| 2020-08-25 | [Implementing transfer learning across different datasets for time series forecasting](https://doi.org/10.1016/j.patcog.2020.107617) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Rui Ye, Qun Dai | 129 | cites it |
| 2019-04-17 | [A simple combination of univariate models](https://doi.org/10.1016/j.ijforecast.2019.01.006) | [International Journal of Forecasting](https://scholar4ai.com/journals/international-journal-of-forecasting.md) | Fotios Petropoulos, Ivan Svetunkov | 80 | cites it |
| 2018-01-02 | [Forecasting global stock market implied volatility indices](https://doi.org/10.1016/j.jempfin.2017.12.008) | [Journal of Empirical Finance](https://scholar4ai.com/journals/journal-of-empirical-finance.md) | Stavros Antonios Degiannakis, George N. Filis, Hossein Hassani | 58 | cites it |
| 2022-01-19 | [Vaccination strategies and transmission of COVID-19: Evidence across advanced countries](https://doi.org/10.1016/j.jhealeco.2022.102589) | [Journal of Health Economics](https://scholar4ai.com/journals/journal-of-health-economics.md) | Dongwoo Kim, Young Jun Lee | 43 | cites it |
| 2017-02-22 | [Heuristic decision rules for short-term trading of renewable energy with co-located energy storage](https://doi.org/10.1016/j.cor.2016.12.027) | [Computers & Operations Research](https://scholar4ai.com/journals/computers-operations-research.md) | Michael Hassler | 32 | cites it |
| 2018-06-18 | [Rare earth elements price forecasting by means of transgenic time series developed with ARIMA models](https://doi.org/10.1016/j.resourpol.2018.06.003) | [Resources Policy](https://scholar4ai.com/journals/resources-policy.md) | María Victoria Riesgo García, Alicja Krzemień, Miguel Ángel Manzanedo del Campo et al. | 31 | cites it |
| 2021-09-18 | [Measuring systemic risk and contagion in the European financial network](https://doi.org/10.1007/s00181-021-02135-y) | [Empirical Economics](https://scholar4ai.com/journals/empirical-economics.md) | Laleh Tafakori, Armin Pourkhanali, Riccardo Rastelli | 25 | cites it |
| 2018-12-07 | [Intraday forecasts of a volatility index: functional time series methods with dynamic updating](https://doi.org/10.1007/s10479-018-3108-4) | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | Han Lin Shang, Yang Yang, Fearghal Kearney | 22 | cites it |
| 2023-09-18 | [An algorithm for automatic selection and combination of forecast models](https://doi.org/10.1016/j.eswa.2023.121636) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Carlos García-Aroca, María Asunción Martínez Mayoral, Javier Morales et al. | 8 | cites it |
| 2019-03-12 | [Seasonal time series forecasting by the Walsh-transformation based technique](https://doi.org/10.1007/s10100-019-00614-3) | [Central European Journal of Operations Research](https://scholar4ai.com/journals/central-european-journal-of-operations-research.md) | Erik B. Bajalinov, Szabolcs Duleba | 5 | cites it |
| 2024-05-06 | [The future of regional inequalities: an ARIMA forecast](https://doi.org/10.1007/s12076-024-00379-7) | [Letters in Spatial and Resource Sciences](https://scholar4ai.com/journals/letters-in-spatial-and-resource-sciences.md) | Hasan Engìn Duran | 5 | cites it |
| 2025-09-23 | [Marketing predictions under macroeconomic volatility: empirical evidence for automotive SMEs from a machine learning perspective](https://doi.org/10.1057/s41270-025-00436-0) | [Journal of Marketing Analytics](https://scholar4ai.com/journals/journal-of-marketing-analytics.md) | Manuel Muth, Anita Talitha Parsegyan, Julian Litzinger et al. | 3 | cites it |
| 2025-08-22 | [Spindle thermal error modeling with linear and nonlinear hybrid tandem metamodel based on statistical and machine learning approaches](https://doi.org/10.1016/j.eswa.2025.129449) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Menghua Li, Mingchang Chih, Chia-Fu Kou et al. | 2 | cites it |
| 2023-07-11 | [Primary market demand for German government bonds](https://doi.org/10.1016/j.jimonfin.2023.102909) | [Journal of International Money and Finance](https://scholar4ai.com/journals/journal-of-international-money-and-finance.md) | Jakob Shida | 1 | cites it |
| 2025-06-04 | [The future of urban hierearchy and Zipf law: ARIMA and BATS forecasting](https://doi.org/10.1007/s12076-025-00408-z) | [Letters in Spatial and Resource Sciences](https://scholar4ai.com/journals/letters-in-spatial-and-resource-sciences.md) | Hasan Engìn Duran | 1 | cites it |
| 2026-06-13 | [Market analytics beyond big data: pretrained AI models for industrial order forecasting in Germany](https://doi.org/10.1057/s41270-026-00489-9) | [Journal of Marketing Analytics](https://scholar4ai.com/journals/journal-of-marketing-analytics.md) | Manuel Muth, Jonas Engel, Gerd Nufer | 1 | cites it |
| 2025-10-27 | [Threshold Vector Error Correction for Modeling the Changes in Gasoline Price with Data Imputation](https://doi.org/10.1007/s10690-025-09576-w) | [Asia-Pacific Financial Markets](https://scholar4ai.com/journals/asia-pacific-financial-markets.md) | Ryan S. Gill, Rasitha R. Jayasekare, Kiseop Lee | 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). forecast (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-forecast

```bibtex
@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}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).