Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/76661
Authors: 
Ardelean, Vlad
Pleier, Thomas
Year of Publication: 
2013
Series/Report no.: 
IWQW Discussion Paper Series 05/2013
Abstract: 
Nonparametric prediction of time series is a viable alternative to parametric prediction, since parametric prediction relies on the correct specification of the process, its order and the distribution of the innovations. Often these are not known and have to be estimated from the data. Another source of nuisance can be the occurrence of outliers. By using nonparametric methods we circumvent both problems, the specification of the processes and the occurrence of outliers. In this article we compare the prediction power for parametric prediction, semiparametric prediction and nonparamatric methods such as support vector machines and pattern recognition. To measure the prediction power we use the MSE. Furthermore we test if the increase in prediction power is statistically significant.
Subjects: 
Parametric prediction
Nonparametric prediction
Support Vector Regression
Outliers
Document Type: 
Working Paper

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