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

• Type: Conference
• Year: 2005
• Where: ICANN
• Authors: Antti Sorjamaa, Jin Hao, Amaury Lendasse
• Keywords: ICANN 2005, Input Selection, K-nearest Neighbors Approximator, Time Series