Modelling Seasonality and Trends in Daily Rainfall Data
Abstract
This paper presents a new approach to the problem of modelling daily rainfall using neural networks. We first model the conditional distribu(cid:173) tions of rainfall amounts, in such a way that the model itself determines the order of the process, and the time-dependent shape and scale of the conditional distributions. After integrating over particular weather pat(cid:173) terns, we are able to extract seasonal variations and long-term trends.
Cite
Text
Williams. "Modelling Seasonality and Trends in Daily Rainfall Data." Neural Information Processing Systems, 1997.Markdown
[Williams. "Modelling Seasonality and Trends in Daily Rainfall Data." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/williams1997neurips-modelling/)BibTeX
@inproceedings{williams1997neurips-modelling,
title = {{Modelling Seasonality and Trends in Daily Rainfall Data}},
author = {Williams, Peter M.},
booktitle = {Neural Information Processing Systems},
year = {1997},
pages = {985-991},
url = {https://mlanthology.org/neurips/1997/williams1997neurips-modelling/}
}