Abstract. Mining evolving datastreams raises the question how to extrapolate trends in the evolution of densities over time. While approaches for change diagnosis work well for interpolating spatio-temporal densities, they are not designed for extrapolation tasks. This work studies the temporal density extrapolation problem and sketches two approaches that addresses it. Both use a set of pseudo-points in combination with spatio-temporal kernel density estimation. The first, weightextrapolating approach, uses regression on the weights of stationarylocated pseudo-points. The second, location-extrapolating approach, extrapolates the trajectory of uniformly-weighted pseudo-points within the feature space.