Time Series Forecasting Using Wavelet-Least Squares Support Vector Machines and Wavelet Regression Models for Monthly Stream Flow Data — Oak Academic Publishing
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Time Series Forecasting Using Wavelet-Least Squares Support Vector Machines and Wavelet Regression Models for Monthly Stream Flow Data
Faculty of Science, Department of Mathematics, Universiti Teknologi Malaysia, Skudai, Malaysia
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Faculty of Science, Department of Mathematics, Universiti Teknologi Malaysia, Skudai, Malaysia
1 Faculty of Science, Department of Mathematics, Universiti Teknologi Malaysia, Skudai, Malaysia
2 Faculty of Science, Department of Mathematics, Universiti Teknologi Malaysia, Skudai, Malaysia
This study explores the least square support vector and wavelet technique (WLSSVM) in the monthly stream flow fore casting. This is a new hybrid technique. The 30 day s periodic predicting statistics used in this study are derived from the subjection of this model to the river flow data of the Jhelum and Chenab rivers. The root mean square error (RMSE), mean absolute error (RME) and correlation (R) statistics are used for evaluating the accuracy of the WLSSVM and WR models. The accuracy of the WLSSVM model is compared with LSSVM, WR and LR models. The two rivers surveyed are in the Republic of Pakistan and cover an area encompassing 39 , 200 km 2 for the Jhelum River and 67 , 515 km 2 for the Chenab River. Using discrete wavelets, the observed data has been decomposed into sub-series. These have then appropriately been used as inputs in the least square support vector machines for forecasting the hydrological variables. The resultant observation from this comparison indicates the WLSSVM is more accurate than the LSSVM, WR and LR models in river flow forecasting.
KeywordsRiver FlowTime SeriesLeast Square Support MachinesWavelet
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