Short-term rainfall time series prediction with incomplete data

C. RIVEROS; H. D. PATIÑO; J. PUCHETA

Killarney

Congreso; 2015 International Joint Conference on Neural Networks (IJCNN); 2015

IEEE

In order to predict short-term times series with incomplete data, a proposed approach is presented based on the energy associated of series. A benchmark of rainfall time series and Mackay Glass (MG) samples are used. An average smoothing technique is adopted to complete the dataset. Thestructure of the predictor filter is changed taking into account the energy associated of the short series. The H parameter is used to estimate the roughness of the complete series, the real andforecasted one. The next 15 values are used as validation and horizon of the time series presented by series of cumulative monthly historical rainfall from La Sevillana, Cordoba, Argentina and  samples of the Mackay Glass (M G) differential equation. The performance of the proposed filter shows that even the short dataset is incomplete, besides a linear smoothing technique employed, the prediction is almost fair. Although the major result shows that the predictor system based on energy associated to series has an optimal performance from several samples of MG equations and, in particular, MG1.6 and SEV rainfall time series, this method provides a good estimation when the short-term series are taken from one point observations.