PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Mutual information and k-nearest neighbors approximator for time series prediction
Antti Sorjamaa, Jin Hao and Amaury Lendasse
In: Artificial Neural Networks: Biological Inspirations – ICANN 2005: 15th International Conference, Warsaw, Poland, September 11-15, 2005. Proceedings, Part II Lecture Notes in Computer Science , 3697 (XXXII). (2005) Springer-Verlag GmbH , Germany , pp. 553-558. ISBN 3-540-28755-8

Abstract

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.

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Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1678
Deposited By:Amaury Lendasse
Deposited On:28 November 2005