  Solar Energetic Particle (SEP) events and their major subclass, Solar Proton
Events (SPEs), can have unfavorable consequences on numerous aspects of life
and technology, making them one of the most harmful effects of solar activity.
Garnering knowledge preceding such events by studying operational data flows is
essential for their forecasting. Considering only Solar Cycle (SC) 24 in our
previous study, Sadykov et al. 2021, we found that it may be sufficient to
utilize only proton and soft X-ray (SXR) parameters for SPE forecasts. Here, we
report a catalog recording $\geq$ 10 MeV $\geq$ 10 particle flux unit SPEs with
their properties, spanning SCs 22-24, using NOAA's Geostationary Operational
Environmental Satellite flux data. We report an additional catalog of daily
proton and SXR flux statistics for this period, employing it to test the
application of machine learning (ML) on the prediction of SPEs using a Support
Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost). We explore the
effects of training models with data from one and two SCs, evaluating how
transferable a model can be across different time periods. XGBoost proved to be
more accurate than SVMs for almost every test considered, while outperforming
operational SWPC NOAA predictions and a persistence forecast. Interestingly,
training done with SC 24 produces weaker TSS and HSS2, even when paired with SC
22 or SC 23, indicating transferability issues. This work contributes towards
validating forecasts using long-spanning data -- an understudied area in SEP
research that should be considered to verify the cross-cycle robustness of
ML-driven forecasts.
