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Updated references in vignettes, index, readme. Updated index links.
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dazzimonti committed Nov 26, 2024
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1 change: 1 addition & 0 deletions .Rbuildignore
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^README\.Rmd$
^TODO\.Rmd$
^TODO\.md$
^index\.md$
^examples$
^\.github$
^codecov\.yml$
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9 changes: 5 additions & 4 deletions README.Rmd
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Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024).
*Properties of the reconciled distributions for Gaussian and count forecasts*.
International Journal of Forecasting (in press).
International Journal of Forecasting 40 (4), 1438-1448.
[DOI](https://doi.org/10.1016/j.ijforecast.2023.12.004)

Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024).
*Probabilistic reconciliation of mixed-type hierarchical time series*.
The 40th Conference on Uncertainty in Artificial Intelligence, accepted.
Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024).
*Probabilistic reconciliation of mixed-type hierarchical time series*.
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence,
in Proceedings of Machine Learning Research 244:4078-4095. [Available here](https://proceedings.mlr.press/v244/zambon24a.html).

## Contributors

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6 changes: 4 additions & 2 deletions README.md
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Expand Up @@ -275,12 +275,14 @@ Statistics and Computing 34 (1), 21.

Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). *Properties of
the reconciled distributions for Gaussian and count forecasts*.
International Journal of Forecasting (in press).
International Journal of Forecasting 40 (4), 1438-1448.
[DOI](https://doi.org/10.1016/j.ijforecast.2023.12.004)

Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024).
*Probabilistic reconciliation of mixed-type hierarchical time series*.
The 40th Conference on Uncertainty in Artificial Intelligence, accepted.
Proceedings of the Fortieth Conference on Uncertainty in Artificial
Intelligence, in Proceedings of Machine Learning Research 244:4078-4095.
[Available here](https://proceedings.mlr.press/v244/zambon24a.html).

## Contributors

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12 changes: 6 additions & 6 deletions index.md
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## Getting started

The starting point for `bayesRecon` functions is the vignette in the "Get Started"
section of the website. Moreover you can find the documentation in the
Reference section and additional vignettes in the Articles section.
Finally a short example is provided below.
The starting point for `bayesRecon` functions is the vignette ["Get Started"](https://idsia.github.io/bayesRecon/articles/bayesRecon.html).
You can all find documentation in [Reference section](https://idsia.github.io/bayesRecon/reference/index.html) and additional vignettes in the Articles section.
Finally a short example with code is provided below.


## Installation
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Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). *Properties of
the reconciled distributions for Gaussian and count forecasts*.
International Journal of Forecasting (in press).
International Journal of Forecasting 40 (4), 1438-1448.
[DOI](https://doi.org/10.1016/j.ijforecast.2023.12.004)

Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024).
*Probabilistic reconciliation of mixed-type hierarchical time series*.
The 40th Conference on Uncertainty in Artificial Intelligence, accepted.
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence,
in Proceedings of Machine Learning Research 244:4078-4095. [Available here](https://proceedings.mlr.press/v244/zambon24a.html).
32 changes: 25 additions & 7 deletions vignettes/references.bib
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@article{zambon2024properties,
title = {Properties of the reconciled distributions for Gaussian and count forecasts},
journal = {International Journal of Forecasting},
volume = {40},
number = {4},
pages = {1438-1448},
year = {2024},
issn = {0169-2070},
author = {Lorenzo Zambon and Arianna Agosto and Paolo Giudici and Giorgio Corani}
doi = {https://doi.org/10.1016/j.ijforecast.2023.12.004},
url = {https://www.sciencedirect.com/science/article/pii/S016920702300136X},
author = {Lorenzo Zambon and Arianna Agosto and Paolo Giudici and Giorgio Corani},
keywords = {Reconciliation, Hierarchical forecasting, Importance sampling, Intermittent time series, Probabilistic forecasts}
}




@article{corani2023probabilistic,
title={Probabilistic reconciliation of count time series},
author={Corani, Giorgio and Azzimonti, Dario and Rubattu, Nicol{\`o}},
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year={2022}
}

@inproceedings{zambon2024mixed,
title={Probabilistic reconciliation of mixed-type hierarchical time series},
author={Zambon, Lorenzo and Azzimonti, Dario and Rubattu, Nicolò and Corani, Giorgio},
booktitle={The 40th Conference on Uncertainty in Artificial Intelligence},
year={2024}

@InProceedings{zambon2024mixed,
title = {Probabilistic reconciliation of mixed-type hierarchical time series},
author = {Zambon, Lorenzo and Azzimonti, Dario and Rubattu, Nicol\`o and Corani, Giorgio},
booktitle = {Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence},
pages = {4078--4095},
year = {2024},
editor = {Kiyavash, Negar and Mooij, Joris M.},
volume = {244},
series = {Proceedings of Machine Learning Research},
month = {15--19 Jul},
publisher = {PMLR},
pdf = {https://raw.githubusercontent.com/mlresearch/v244/main/assets/zambon24a/zambon24a.pdf},
url = {https://proceedings.mlr.press/v244/zambon24a.html}
}



@article{MAKRIDAKIS20221325,
title = {{The M5 competition: Background, organization, and implementation}},
journal = {International Journal of Forecasting},
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