---
title: "web-traffic-time-series-forecasting: papers citing it"
description: "3 papers in the tracked journals link to the Kaggle dataset web-traffic-time-series-forecasting in their Crossref records, the first in 2021; most of them (2) in 2021."
url: https://scholar4ai.com/code/kaggle-competition-web-traffic-time-series-forecasting
markdown_url: https://scholar4ai.com/code/kaggle-competition-web-traffic-time-series-forecasting.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "3 papers in the tracked journals link to the Kaggle dataset web-traffic-time-series-forecasting in their Crossref records, the first in 2021; most of them (2) in 2021. (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)
---

# web-traffic-time-series-forecasting: papers linking to it

Research papers in the 480 tracked journals that link to web-traffic-time-series-forecasting (https://www.kaggle.com/competitions/web-traffic-time-series-forecasting), 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.

- Kaggle
- https://www.kaggle.com/competitions/web-traffic-time-series-forecasting

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

## Key findings

- 3 papers in the tracked journals link to the Kaggle dataset web-traffic-time-series-forecasting in their Crossref records, the first in 2021; most of them (2) in 2021.
- By category: AI (3); the journals with the most are Expert Systems with Applications (1), Pattern Recognition (1) and Machine Learning (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links web-traffic-time-series-forecasting.

## By year

*Papers linking to web-traffic-time-series-forecasting by publication year*

| Year | Papers |
|---|---:|
| 2021 | 2 |
| 2023 | 1 |

## Journals

*Journals whose papers link to web-traffic-time-series-forecasting most*

| Journal | Papers |
|---|---:|
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |
| [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 1 |
| [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Global models for time series forecasting: A Simulation study](https://doi.org/10.1016/j.patcog.2021.108441) (Pattern Recognition, 2021)
  - [SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting](https://doi.org/10.1007/s10994-023-06316-x) (Machine Learning, 2023) — cites the paper above

## Papers

*Papers linking to web-traffic-time-series-forecasting, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2021-11-20 | [Global models for time series forecasting: A Simulation study](https://doi.org/10.1016/j.patcog.2021.108441) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara | 83 | cites it |
| 2021-11-08 | [A comparative study of nonlinear regression and autoregressive techniques in hybrid with particle swarm optimization for time-series forecasting](https://doi.org/10.1016/j.eswa.2021.116163) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Cry Kuranga, Nelishia Pillay | 29 | cites it |
| 2023-03-13 | [SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting](https://doi.org/10.1007/s10994-023-06316-x) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Rakshitha Godahewa, Geoffrey I. Webb, Daniel Francis Schmidt et al. | 15 | 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). web-traffic-time-series-forecasting: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-competition-web-traffic-time-series-forecasting

```bibtex
@misc{scholar4ai-code-kaggle-competition-web-traffic-time-series-forecasting,
  author = {{Scholar4AI}},
  title = {web-traffic-time-series-forecasting: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-competition-web-traffic-time-series-forecasting}},
  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/).