Practical results show that, by using wavelet decomposition and reconstruction, this method can efficiently extract weak deformation characteristics from the observed data series having strong noises.
结果表明，借助于小波分解与重构，可有效地从强噪声干扰的观测数据序列中提取变形特征。
The results show that when to data to be observed is unequal interval, it is more effective for the grey-chaos time series to analyse the data.
结果表明，当观测数据为非等时序列，采用灰色混沌时间序列进行分析比较有效；
We exploit the above prediction model validate effect while there are omitted data in observed time series and propose the corresponding resolve.
应用基于邻近点的非线性自适应预测模型验证了观测时间序列存在数据缺损时的预测效果，并提出了相应的解决办法。
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