  Conditional Generative Adversarial Networks~(CGAN) are a recent and popular
method for generating samples from a probability distribution conditioned on
latent information. The latent information often comes in the form of a
discrete label from a small set. We propose a novel method for training CGANs
which allows us to condition on a sequence of continuous latent distributions
$f^{(1)}, \ldots, f^{(K)}$. This training allows CGANs to generate samples from
a sequence of distributions. We apply our method to paintings from a sequence
of artistic movements, where each movement is considered to be its own
distribution. Exploiting the temporal aspect of the data, a vector
autoregressive (VAR) model is fitted to the means of the latent distributions
that we learn, and used for one-step-ahead forecasting, to predict the latent
distribution of a future art movement $f^{{(K+1)}}$. Realisations from this
distribution can be used by the CGAN to generate "future" paintings. In
experiments, this novel methodology generates accurate predictions of the
evolution of art. The training set consists of a large dataset of past
paintings. While there is no agreement on exactly what current art period we
find ourselves in, we test on plausible candidate sets of present art, and show
that the mean distance to our predictions is small.
