 Geoscientific measurements often provide time series with irregular time sampling, requiring either data reconstruction, interpolation, or sophisticated methods to handle irregular sampling. We compared the linear interpolation technique in different approaches for analyzing the correlation functions and persistence of irregularly sampled time series, as Lohm-Scargill Fourier transformation and kernel-based methods. In a thorough benchmark test, we investigated the performance of these techniques. All methods had comparable root mean square errors, RMSCs, for low skewness of the inter-observation time distribution. For high skewness, very irregular data, interpolation bias, and RMSC increase strongly. We found a 40% lower RMSC for the lag, one autocorrelation function ACF, for the Gaussian kernel method versus the linear interpolation scheme, in the analysis of highly irregular time series. For the cross-correlation function, CCF, the RMSC was then lowered by 60%. The application of the Lohm-Scargill technique gave results comparable to the kernel methods for the univariate, but poorer results in the bivariate case. Especially the high-frequency components of the signal, where classical methods showed. This article was authored by K. Refeld and Marwan, J. Heitzig, and others. We are article.tv, links in the description below.