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.