An adaptive beamformer may be thought of as trading white noise gain for interferer suppression. The beamformer can respond to changing environmental statistics through updates to the sample covariance matrix. In time-varying environments, adaptive beamformers are frequently used with pre-determined sliding windows or forgetting factors for such sample covariance estimation. Thus, an adaptive beamformer must a priori select the regions over which the data are assumed stationary. Such methods perform poorly when the environment suddenly changes, such as strong interferers entering or exiting the acoustic scene. Many real-world environments have intermittent interferers, and such a beamformer may waste degrees of freedom suppressing an interferer that is no longer active or neglecting to suppress one that is. We propose the use of universal methods over a class of time-partitioned beamformers. While there are an exponential number of possible partitions of a block of data into locally stationary regions, methods from universal data compression and prediction for piece-wise stationary sources provide a path for implicitly implementing, and mixing over them all, with only polynomial complexity. We employ a linear transition diagram from this literature to enable efficient performance-weighted mixing of beamformers of all possible such partitions.