Title :
Variable Scaling for Time Series Prediction: Application to the ESTSP´07 and the NN3 Forecasting Competitions
Author :
Lendasse, Amaury ; Liitiainen, Elia
Author_Institution :
Helsinki Univ. of Technol., Helsinki
Abstract :
In this paper, variable selection and variable scaling are used in order to select the best regressor for the problem of time series prediction. Direct prediction methodology is used instead of the classic recursive methodology. Least Squares Support Vector Machines (LS-SVM) and K-NN approximator are used in order to avoid local minimal in the training phase of the model. The global methodology is applied to the ESTSP´07 competition dataset and the dataset B of the NN3 Forecasting Competition.
Keywords :
least squares approximations; mathematics computing; support vector machines; time series; ESTSP´07 competition dataset; K-NN approximator; NN3 forecasting competition; direct prediction methodology; least square support vector machine; recursive methodology; time series prediction; Economic forecasting; Input variables; Least squares approximation; Load forecasting; Predictive models; Stock markets; Support vector machines; Testing; Uncertainty; Yttrium;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4371405
