Bivariate time series which display nonstationary behavior, such as cycles or long-term trends, are common in fields such as oceanography and meteorology. These are usually very large-scale data sets and often may contain long gaps of missing values in one or both series, with the gaps perhaps occurring at different time periods in the two series. We present a simplified but effective method of interactively examining and filling in the missing values in such series using extensions of the methods available in AGSS, an APL2-based statistical software package. Our method allows for possible detrending and removal of seasonal components before automatically estimating arbitrary patterns of missing values for each series. Interactive bivariate spectral analysis can then be performed on the detrended and deseasonalized interpolated data if desired. We illustrate our results using a bivariate time series of ocean current velocities measured off the California coast. Time series; interpolation; bivariate.