Blinowska K J, Malinowski M
Laboratory of Medical Physics, Warsaw University, Poland.
Biol Cybern. 1991;66(2):159-65. doi: 10.1007/BF00243291.
The method of non-linear forecasting of time series was applied to different simulated signals and EEG in order to check its ability of distinguishing chaotic from noisy time series. The goodness of prediction was estimated, in terms of the correlation coefficient between forecasted and real time series, for non-linear and autoregressive (AR) methods. For the EEG signal both methods gave similar results. It seems that the EEG signal, in spite of its chaotic character, is well described by the AR model.
为检验非线性时间序列预测方法区分混沌时间序列和噪声时间序列的能力，将该方法应用于不同的模拟信号和脑电图（EEG）。针对非线性方法和自回归（AR）方法，根据预测时间序列与实际时间序列之间的相关系数评估预测的优度。对于脑电图信号，两种方法得到了相似的结果。尽管脑电图信号具有混沌特性，但自回归模型似乎能很好地描述它。
