In previous work, we have proposed a constructive methodology for temporal data learning supported by results and prescriptions related to the Embedding Theorem, and using the Singular Spectrum Analysis both in order to reduce the effects of the possible discontinuity of the signal and to implement an efficient ensemble method. In this paper we present new results concerning the application of this approach to the forecasting of the individual rainfall intensities series collected by 135 stations distributed in the Tiber basin. The average RMS of the obtained predictions is less than 3 mm of rain
Application of an Ensemble Technique based on Singular Spectrum Analysis to Daily Rainfall Forecasting
2003
Abstract
In previous work, we have proposed a constructive methodology for temporal data learning supported by results and prescriptions related to the Embedding Theorem, and using the Singular Spectrum Analysis both in order to reduce the effects of the possible discontinuity of the signal and to implement an efficient ensemble method. In this paper we present new results concerning the application of this approach to the forecasting of the individual rainfall intensities series collected by 135 stations distributed in the Tiber basin. The average RMS of the obtained predictions is less than 3 mm of rainFile in questo prodotto:
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