Title :
Performance of Radial Basis Function and Support Vector Machine in time series forecasting
Author :
Mamat, Mazlina ; Samad, Salina Abdul
Author_Institution :
Inst. of Microengineering & Nanotechnol., Univ. Kebangsaan Malaysia, Bangi, Malaysia
Abstract :
This paper compares the performance of Radial Basis Function and Support Vector Regression in time series forecasting. Both methods were trained to produce one step ahead forecasting on two chaotic time series data: Mackey Glass and Set A data from Santa Fe Competition. The criterions for comparison are based on the coefficient of determination (R2) and Root Mean Square Error (RMSE) between actual and forecasted output. Results show that SVR outperformed RBF significantly on both data particularly on Set A data.
Keywords :
forecasting theory; mean square error methods; radial basis function networks; regression analysis; support vector machines; time series; Mackey Glass data forecasting; Set A data forecasting; chaotic time series data forecasting; determination coefficient; radial basis function; root mean square error; support vector machine; support vector regression; Artificial neural networks; Forecasting; Glass; Mathematical model; Support vector machines; Time series analysis; Training;
Conference_Titel :
Intelligent and Advanced Systems (ICIAS), 2010 International Conference on
Conference_Location :
Kuala Lumpur, Malaysia
Print_ISBN :
978-1-4244-6623-8
DOI :
10.1109/ICIAS.2010.5716201
