---
title: "robjhyndman/forecast: papers citing this GitHub repository"
description: "11 papers in the tracked journals link to the GitHub repository robjhyndman/forecast in their Crossref records, the first in 2018; most of them (4) in 2019."
url: https://scholar4ai.com/code/github-robjhyndman-forecast
markdown_url: https://scholar4ai.com/code/github-robjhyndman-forecast.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "11 papers in the tracked journals link to the GitHub repository robjhyndman/forecast in their Crossref records, the first in 2018; most of them (4) in 2019. (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)
---

# robjhyndman/forecast: papers linking to it

Research papers in the 480 tracked journals that link to robjhyndman/forecast (https://github.com/robjhyndman/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.

- GitHub
- 1,186 stars
- 336 forks
- R
- created 2012
- https://github.com/robjhyndman/forecast

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

## Key findings

- 11 papers in the tracked journals link to the GitHub repository robjhyndman/forecast in their Crossref records, the first in 2018; most of them (4) in 2019.
- By category: Finance (5), Operations Research (3) and Economics (2); the journals with the most are Finance research letters (2), European Journal of Operational Research (2) and Journal of International Money and Finance (1).
- 6 of the papers build on one another: a later paper cites an earlier one that also links robjhyndman/forecast.

## By year

*Papers linking to robjhyndman/forecast by publication year*

| Year | Papers |
|---|---:|
| 2018 | 2 |
| 2019 | 4 |
| 2020 | 3 |
| 2022 | 1 |
| 2026 | 1 |

## Journals

*Journals whose papers link to robjhyndman/forecast most*

| Journal | Papers |
|---|---:|
| [Finance research letters](https://scholar4ai.com/journals/finance-research-letters.md) | 2 |
| [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | 2 |
| [Journal of International Money and Finance](https://scholar4ai.com/journals/journal-of-international-money-and-finance.md) | 1 |
| [Portuguese Economic Journal](https://scholar4ai.com/journals/portuguese-economic-journal.md) | 1 |
| [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Exploring the sources of uncertainty: Why does bagging for time series forecasting work?](https://doi.org/10.1016/j.ejor.2018.01.045) (European Journal of Operational Research, 2018)
  - [Structural combination of seasonal exponential smoothing forecasts applied to load forecasting](https://doi.org/10.1016/j.ejor.2018.12.013) (European Journal of Operational Research, 2018) — cites the paper above
- [Forecasting realized gold volatility: Is there a role of geopolitical risks?](https://doi.org/10.1016/j.frl.2019.08.028) (Finance research letters, 2019)
  - [A note on investor happiness and the predictability of realized volatility of gold](https://doi.org/10.1016/j.frl.2020.101614) (Finance research letters, 2020) — cites the paper above
- [Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss](https://doi.org/10.1016/j.jimonfin.2020.102137) (Journal of International Money and Finance, 2020)
  - [Forecasting Realized Volatility of Bitcoin: The Role of the Trade War](https://doi.org/10.1007/s10614-020-10022-4) (Computational Economics, 2020) — cites the paper above

## Papers

*Papers linking to robjhyndman/forecast, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2018-02-02 | [Exploring the sources of uncertainty: Why does bagging for time series forecasting work?](https://doi.org/10.1016/j.ejor.2018.01.045) | [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | Fotios Petropoulos, Rob J. Hyndman, Christoph Bergmeir | 162 | cites it |
| 2018-12-08 | [Structural combination of seasonal exponential smoothing forecasts applied to load forecasting](https://doi.org/10.1016/j.ejor.2018.12.013) | [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | Juan F. Rendon‐Sanchez, Lilian M. de Menezes | 156 | cites it |
| 2020-01-25 | [Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss](https://doi.org/10.1016/j.jimonfin.2020.102137) | [Journal of International Money and Finance](https://scholar4ai.com/journals/journal-of-international-money-and-finance.md) | Κωνσταντίνος Γκίλλας, Rangan Gupta, Christian Pierdzioch | 146 | cites it |
| 2019-08-28 | [Forecasting realized gold volatility: Is there a role of geopolitical risks?](https://doi.org/10.1016/j.frl.2019.08.028) | [Finance research letters](https://scholar4ai.com/journals/finance-research-letters.md) | Κωνσταντίνος Γκίλλας, Rangan Gupta, Christian Pierdzioch | 127 | cites it |
| 2020-08-10 | [Forecasting Realized Volatility of Bitcoin: The Role of the Trade War](https://doi.org/10.1007/s10614-020-10022-4) | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Elie I. Bouri, Κωνσταντίνος Γκίλλας, Rangan Gupta et al. | 63 | cites it |
| 2019-08-17 | [Time-varying risk aversion and realized gold volatility](https://doi.org/10.1016/j.najef.2019.101048) | [The North American Journal of Economics and Finance](https://scholar4ai.com/journals/the-north-american-journal-of-economics-and-finance.md) | Rıza Demirer, Κωνσταντίνος Γκίλλας, Rangan Gupta et al. | 51 | cites it |
| 2020-05-28 | [A note on investor happiness and the predictability of realized volatility of gold](https://doi.org/10.1016/j.frl.2020.101614) | [Finance research letters](https://scholar4ai.com/journals/finance-research-letters.md) | Matteo Bonato, Κωνσταντίνος Γκίλλας, Rangan Gupta et al. | 28 | 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 |
| 2019-11-14 | [Forecasting Ontario Oncology Drug Expenditures: A Hybrid Approach to Improving Accuracy](https://doi.org/10.1007/s40258-019-00533-z) | [Applied Health Economics and Health Policy](https://scholar4ai.com/journals/applied-health-economics-and-health-policy.md) | Paula Murray, Yusuf Shalaby, Luciano Ieraci et al. | 2 | cites it |
| 2022-08-11 | [Welfare and sectoral productivity shifts in a small open economy with imported agricultural inputs: The case of Sub-Saharan Africa](https://doi.org/10.1007/s10258-022-00218-x) | [Portuguese Economic Journal](https://scholar4ai.com/journals/portuguese-economic-journal.md) | Juan Antonio García Cebro, Alejandro Quintela-del-Rı́o, Ramón Varela Santamaría | 1 | cites it |
| 2026-05-14 | [Hybrid time series modelling for enhancing price predictability in agricultural markets](https://doi.org/10.1057/s41272-026-00592-7) | [Journal of Revenue and Pricing Management](https://scholar4ai.com/journals/journal-of-revenue-and-pricing-management.md) | Joginder Kumar, Pushpa Ghiyal | 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/forecast: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-robjhyndman-forecast

```bibtex
@misc{scholar4ai-code-github-robjhyndman-forecast,
  author = {{Scholar4AI}},
  title = {robjhyndman/forecast: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-robjhyndman-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/).