PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Pruned lazy learning models for time series prediction
Antti Sorjamaa, Amaury Lendasse and Michel Verleysen
In: ESANN 2005, European Symposium on Artificial Neural Networks, 27-29 April 2005, Bruges, Belgium.


This paper presents two improvements of Lazy Learning. Both methods include input selection and are applied to the long-term prediction of time series. The first method is based on an iterative pruning of the inputs; the second one performs a brute force search in the possible set of inputs using a k-NN approximator. Two benchmarks are used to illustrate the efficiency of these two methods: the Santa Fe A and the CATS Benchmark time series.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1685
Deposited By:Amaury Lendasse
Deposited On:28 November 2005