  Point processes have been dominant in modeling the evolution of seismicity
for decades, with the Epidemic Type Aftershock Sequence (ETAS) model being most
popular. Recent advances in machine learning have constructed highly flexible
point process models using neural networks to improve upon existing parametric
models. We investigate whether these flexible point process models can be
applied to short-term seismicity forecasting by extending an existing temporal
neural model to the magnitude domain and we show how this model can forecast
earthquakes above a target magnitude threshold. We first demonstrate that the
neural model can fit synthetic ETAS data, however, requiring less computational
time because it is not dependent on the full history of the sequence. By
artificially emulating short-term aftershock incompleteness in the synthetic
dataset, we find that the neural model outperforms ETAS. Using a new enhanced
catalog from the 2016-2017 Central Apennines earthquake sequence, we
investigate the predictive skill of ETAS and the neural model with respect to
the lowest input magnitude. Constructing multiple forecasting experiments using
the Visso, Norcia and Campotosto earthquakes to partition training and testing
data, we target M3+ events. We find both models perform similarly at previously
explored thresholds (e.g., above M3), but lowering the threshold to M1.2
reduces the performance of ETAS unlike the neural model. We argue that some of
these gains are due to the neural model's ability to handle incomplete data.
The robustness to missing data and speed to train the neural model present it
as an encouraging competitor in earthquake forecasting.
