A Hybrid Model for Stream Flow Forecasting Using Wavelet and Least Squares Support Vector Machines
DOI:
https://doi.org/10.11113/jt.v73.3380Keywords:
Wavelet, least square support vector machines, artificial neural network, ARIMA, SVMAbstract
This paper proposed a hybrid wavelet-least square support vector machines (WLSSVM) model that combine both wavelet method and LSSVM model for monthly stream flow forecasting. The original stream flow series was decomposed into a number of sub-series of time series using wavelet theory and these time series were imposed as input data to the LSSVM for stream flow forecasting. The monthly stream flow data from Klang and Langat stations in Peninsular Malaysia are used for this case study. Time series prediction capability performance of the WLSSVM model is compared with single LSSVM and Autoregressive Integrated Moving Average (ARIMA) models using various statistical measures. Empirical results showed that the WLSSVM model yield a more accurate outcome compared to individual LSSVM, ANN and ARIMA models for monthly stream flow forecasting.
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