 We compared two methods for analyzing irregularly sampled time series, linear interpolation and Gaussian kernel-based methods. Linear interpolation had higher root mean square errors, RMSCs, than the Gaussian kernel-based methods for low skewness of the inter-observation time distribution. However, for high skewness, the RMSC increased significantly. The Gaussian kernel method had a 40% lower RMSC for the Lag 1 autocorrelation function, ACF, and a 60% lower RMSC for the cross-correlation function, CCF. These results were consistent across all four locations, reflecting late Holocene-Asian monsoon variability as derived from Speliathum-Delta 180 measurements. The Gaussian kernel was also found to be a reliable and more robust estimator with significant advantages compared to other techniques and suitable for large-scale application to Palaeodata. This article was authored by K. Refeld, N. Marwan, J. Heitzig, and others.