Short-term rainfall time series prediction with incomplete data
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Date
2015Author
Rodriguez Rivero, Cristian
Patiño, Hector Daniel
Pucheta, Julian Antonio
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Show full item recordAbstract
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. The
structure 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 and
forecasted 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 (MG) 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.