  This paper focuses on predicting downlink (DL) traffic volume in mobile
networks while minimizing overprovisioning and meeting a given service-level
agreement (SLA) violation rate. We present a multivariate, multi-step, and
SLA-driven approach that incorporates 20 different radio access network (RAN)
features, a custom feature set based on peak traffic hours, and handover-based
clustering to leverage the spatiotemporal effects. In addition, we propose a
custom loss function that ensures the SLA violation rate constraint is
satisfied while minimizing overprovisioning. We also perform multi-step
prediction up to 24 steps ahead and evaluate performance under both single-step
and multi-step prediction conditions. Our study makes several contributions,
including the analysis of RAN features, the custom feature set design, a custom
loss function, and a parametric method to satisfy SLA constraints.
