Precipitation prediction using LS-SVM and ARIMA combined model based on wavelet packet decomposition
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Abstract:
An annual precipitation prediction method is proposed based on wavelet packet decomposition of LS-AVM and ARI- MA combined model because the precipitation has a complex non-stationary, nonlinear, and noisy time series. The wavelet packet is used to decompose the precipitation sequence into a low-frequency trend sequence and high-frequency detail sequence. The LS-SVM model is used to predict the low-frequency trend sequence, and the ARIMA model is used to predict the high-frequency detail sequence. The prediction results of the two models are superimposed to get the predicted value of annual precipitation. The case study shows that: the decomposition of time series by wavelet packet is more precise than the wavelet decomposition, the combined model prediction can comprehensively extract the information contained in the precipitation sequence, better reflect the change of precipitation with time, and improve the annual precipitation forecast which provides a new met hod for the prediction of precipitation.
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