  Deep learning techniques for gravitational-wave parameter estimation have
emerged as a fast alternative to standard samplers $\unicode{x2013}$ producing
results of comparable accuracy. These approaches (e.g., DINGO) enable amortized
inference by training a normalizing flow to represent the Bayesian posterior
conditional on observed data. By conditioning also on the noise power spectral
density (PSD) they can even account for changing detector characteristics.
However, training such networks requires knowing in advance the distribution of
PSDs expected to be observed, and therefore can only take place once all data
to be analyzed have been gathered. Here, we develop a probabilistic model to
forecast future PSDs, greatly increasing the temporal scope of DINGO networks.
Using PSDs from the second LIGO-Virgo observing run (O2) $\unicode{x2013}$ plus
just a single PSD from the beginning of the third (O3) $\unicode{x2013}$ we
show that we can train a DINGO network to perform accurate inference throughout
O3 (on 37 real events). We therefore expect this approach to be a key component
to enable the use of deep learning techniques for low-latency analyses of
gravitational waves.
