Pruned Lazy Learning Models for Time Series Prediction
(2005) ESANN 2005, 13h European Symposium on Artificial Neural Networks — Location: Bruges (Belgium) (27.April.2005)
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- Authors
- 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.
- Affiliations
Helsinki University of Technology Neural Networks Research Center Helsinky University of Technology Neural Networks Research Center
Citations
Sorjamaa, A., Lendasse, A., & Verleysen, M. (2005). Pruned Lazy Learning Models for Time Series Prediction. Proceedings of ESANN 2005, European Symposium on Artificial Neural Networks, p. 509-514. https://hdl.handle.net/2078.5/253728