Modelling Seasonality and Trends in Daily Rainfall Data

Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)

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Peter Williams


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.