Mutual information and k-nearest neighbors approximator for time series prediction

被引：0

作者：

Sorjamaa, A ^{[1
]}

Hao, J ^{[1
]}

Lendasse, A ^{[1
]}

机构：

[1] Helsinki Univ Technol, Neural Network Res Ctr, FIN-02150 Espoo, Finland

来源：

ARTIFICIAL NEURAL NETWORKS: FORMAL MODELS AND THEIR APPLICATIONS - ICANN 2005, PT 2, PROCEEDINGS | 2005年 / 3697卷

关键词：

time series; input selection; mutual information; k-NN;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 for long-term prediction of time series. The Santa Fe A time series is used as an example.

引用

页码：553 / 558

页数：6

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