Pruned lazy learning models for time series prediction
نویسندگان
چکیده
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.
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