Outliers & predicting time series: A comparative study

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IWQW Discussion Papers ; No. 05/2013

Classification and Topics

• Classification: Wirtschaft
• Subject: Parametric prediction ; Nonparametric prediction ; Support Vector Regression ; Outliers

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Ardelean, Vlad ; Pleier, Thomas
• Event: Veröffentlichung
• (who): Friedrich-Alexander-Universität Erlangen-Nürnberg, Institut für Wirtschaftspolitik und Quantitative Wirtschaftsforschung (IWQW)
• (where): Nürnberg
• (when): 2013

Further information

• Handle: http://hdl.handle.net/10419/76661
• Last update: 10.03.2025, 11:42 AM CET