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

Authors:
Antti Sorjamaa;Jin Hao;Amaury Lendasse
Affiliations:
Neural Network Research Centre, Helsinki University of Technology, Espoo, Finland;Neural Network Research Centre, Helsinki University of Technology, Espoo, Finland;Neural Network Research Centre, Helsinki University of Technology, Espoo, Finland
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

Citing 6
Cited 5

Adaptive online learning algorithms for blind separation: maximum entropy and minimum mutual information

Neural Computation
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Feature selection for high-dimensional genomic microarray data

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Algorithms for Feature Selection: An Evaluation

ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
A study of cross-validation and bootstrap for accuracy estimation and model selection

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Input feature selection for classification problems

IEEE Transactions on Neural Networks

High-dimensional delay selection for regression models with mutual information and distance-to-diagonal criteria

Neurocomputing
Design of specific-to-problem kernels and use of kernel weighted K-nearest neighbours for time series modelling

Neurocomputing
OP-KNN: method and applications

Advances in Artificial Neural Systems
Effective input variable selection for function approximation

ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Two-stage approach for electricity consumption forecasting in public buildings

IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis

Quantified Score

Hi-index	0.00

Visualization

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.