  Time series data can be subject to changes in the underlying process that
generates them and, because of these changes, models built on old samples can
become obsolete or perform poorly. In this work, we present a way to
incorporate information about the current data distribution and its evolution
across time into machine learning algorithms. Our solution is based on
efficiently maintaining statistics, particularly the mean and the variance, of
data features at different time resolutions. These data summarisations can be
performed over the input attributes, in which case they can then be fed into
the model as additional input features, or over latent representations learned
by models, such as those of Recurrent Neural Networks. In classification tasks,
the proposed techniques can significantly outperform the prediction
capabilities of equivalent architectures with no feature / latent
summarisations. Furthermore, these modifications do not introduce notable
computational and memory overhead when properly adjusted.
