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Time Series Forecasting Using Wavelet-Least Squares Support Vector Machines and Wavelet Regression Models for Monthly Stream Flow Data

This study explores the least square support vector and wavelet 
technique (WLSSVM) in the monthly stream flow forecasting. This is a 
new hybrid technique. The 30 days 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 km2 for the Jhelum River and 67,515 km2 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.