Input and Structure Selection for k-NN Approximator
نویسندگان
چکیده
This paper presents k-NN as an approximator for time series prediction problems. The main advantage of this approximator is its simplicity. Despite the simplicity, k-NN can be used to perform input selection for nonlinear models and it also provides accurate approximations. Three model structure selection methods are presented: Leave-one-out, Bootstrap and Bootstrap 632. We will show that both Bootstraps provide a good estimate of the number of neighbors, k, where Leave-one-out fails. Results of the methods are presented with the Electric load from Poland data set.
منابع مشابه
Pruned lazy learning models for time series prediction
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...
متن کاملMutual Information and k-Nearest Neighbors Approximator for Time Series Prediction
This paper presents a method that combines Mutual Information and k-Nearest Neighbors approximator for time series prediction. Mutual Information is used for input selection. K-Nearest Neighbors approximator is used to improve the input selection and to provide a simple but accurate prediction method. Due to its simplicity the method is repeated to build a large number of models that are used f...
متن کاملNegative Learning Rates and P-Learning
We present a method of training a differentiable function approximator for a regression task using negative examples. We effect this training using negative learning rates. We also show how this method can be used to perform direct policy learning in a reinforcement learning setting. 1 Regression and Learning Rates The goal of regression analyses is to find a regression function, a function tha...
متن کاملTabu Search with Delta Test for Time Series Prediction using OP-KNN
This paper presents a working combination of input selection strategy and a fast approximator for time series prediction. The input selection is performed using Tabu Search with the Delta Test. The approximation methodology is called Optimally-Pruned k -Nearest Neighbors (OP-KNN), which has been recently developed for fast and accurate regression and classification tasks. In this paper we demon...
متن کاملMethodology for long-term prediction of time series
In this paper, a global methodology for the long-term prediction of time series is proposed. This methodology combines direct prediction strategy and sophisticated input selection criteria: k-nearest neighbors approximation method (k-NN), mutual information (MI) and nonparametric noise estimation (NNE). A global input selection strategy that combines forward selection, backward elimination (or ...
متن کامل