Missing data which inevitably occurs in observed time series may lead to an erroneous result based on the correlation integral analysis. Effects of data, missing at regular and irregular times, on the analyzed result are estimated. A model estimation is obtained for the Lorenz time series. The effects of the missing data in economic and astronomical time series are estimated using the correlation integral analysis. A convenient method of choosing a time lag is proposed to minimize the effect of regularly missing data.

The radio wave intensity time series of the quasar is observed with the radio wave interferometer on the earth. External noise may superimpose with the radio wave on the path of wave propagation over the cosmological distance. In this paper, the effects of the superimposed noise on the radio wave intensity time series are discussed assuming nonlinear dynamics to apply on the time series. A convolution method is applied to the original observed radio wave intensity time series. Both the original and the convolution time series are analyzed by the Grassberger-Procaccia (GP) method with correlation integration and compared the results to estimate the presence and the effects of superimposed subtle noise. In addition, surrogate and Judd methods are applied to the radio wave intensity time series to increase the credibility of the results of the GP method. The effects of added random noise in Lorenz model are also analyzed with the GP method to estimate the above results.