  In recent years, we have seen a handful of work on inference algorithms over
non-stationary data streams. Given their flexibility, Bayesian non-parametric
models are a good candidate for these scenarios. However, reliable streaming
inference under the concept drift phenomenon is still an open problem for these
models. In this work, we propose a variational inference algorithm for
Dirichlet process mixture models. Our proposal deals with the concept drift by
including an exponential forgetting over the prior global parameters. Our
algorithm allows to adapt the learned model to the concept drifts
automatically. We perform experiments in both synthetic and real data, showing
that the proposed model is competitive with the state-of-the-art algorithms in
the density estimation problem, and it outperforms them in the clustering
problem.
