For time series data with outliers, a Robust Generalized Cross-Correlation measure (RGCC) between time series based on robust estimation of correlation coefficient was proposed. First, a robust correlation coefficient was introduced to replace Pearson correlation coefficient to calculate the covariance matrix between time series data. Second, the determinant of the new covariance matrix was used to construct a similarity measure between two time series named RGCC. Finally, the distance matrix between the time series was calculated based on this measure, and the matrix was used as the input of the clustering algorithm to cluster the data. Time series clustering simulation experiments showed that for time series data with outliers, the clustering results based on RGCC were obviously closer to the real ones compared to the clustering results based on the original Generalized Cross-Correlation measure (GCC). It can be seen that the proposed new robust similarity measure is fully applicable to time series data with outliers.