雑音を含むカオス時系列データの予測

概要

論文の詳細を見る
In a local approximation technique for predicting chaotic time series, a state space is reconstructed from a time series using delay coordinates and then a local predictor is constructed on the basis of the motion of the nearest neighbors in the state space. The number of the nearest neighbors has an important effect on the prediction accuracy. We evaluate the prediction error as a function of the number of the nearest neighbors for time series of the Henon map and the Lorenz model contaminated with Gaussian white noise and show basic guidelines for selecting the nearest neighbors.
県立長崎シーボルト大学の論文
2003-12-20