Pruned Lazy Learning Models for Time Series Prediction
Antti Sorjamaa, Amaury Lendasse, Michel Verleysen · 2005
Abstract. This paper presents two improvements of Lazy Learning. Both methods include input selection and are applied to long-term prediction of time series. First method is based on an iterative pruning of the inputs and the second one is performing 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 time series and the CATS Benchmark time series. 1