  Most predictive models assume that training and test data are generated from
a stationary process. However, this assumption does not hold true in practice.
In this paper, we consider the scenario of a gradual concept drift due to the
underlying non-stationarity of the data source. While previous work has
investigated this scenario under a supervised-learning and adaption conditions,
few have addressed the common, real-world scenario when labels are only
available during training. We propose a novel, iterative algorithm for
unsupervised adaptation of predictive models. We show that the performance of
our batch adapted prediction algorithm is better than that of its corresponding
unadapted version. The proposed algorithm provides similar (or better, in most
cases) performance within significantly less run time compared to other state
of the art methods. We validate our claims though extensive numerical
evaluations on both synthetic and real data.
