TITLE:
Time Series Forecasting Using Wavelet-Least Squares Support Vector Machines and Wavelet Regression Models for Monthly Stream Flow Data
AUTHORS:
Siraj Muhammed Pandhiani, Ani Bin Shabri
KEYWORDS:
River Flow; Time Series; Least Square Support Machines; Wavelet
JOURNAL NAME:
Open Journal of Statistics,
Vol.3 No.3,
June
18,
2013
ABSTRACT: 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.