  Understanding the long-term evolution of hierarchical triple systems is
challenging due to its inherent chaotic nature, and it requires computationally
expensive simulations. Here we propose a convolutional neural network model to
predict the stability of hierarchical triples by looking at their evolution
during the first $5 \times 10^5$ inner binary orbits. We employ the regularized
few-body code TSUNAMI to simulate $5\times 10^6$ hierarchical triples, from
which we generate a large training and test dataset. We develop twelve
different network configurations that use different combinations of the
triples' orbital elements and compare their performances. Our best model uses 6
time-series, namely, the semimajor axes ratio, the inner and outer
eccentricities, the mutual inclination and the arguments of pericenter. This
model achieves an area under the curve of over $95\%$ and informs of the
relevant parameters to study triple systems stability. All trained models are
made publicly available, allowing to predict the stability of hierarchical
triple systems $200$ times faster than pure $N$-body methods.
