  Ultra reliable low latency communications (URLLC) is a new service class
introduced in 5G which is characterized by strict reliability $(1-10^{-5})$ and
low latency requirements (1 ms). To meet these requisites, several strategies
like overprovisioning of resources and channel-predictive algorithms have been
developed. This paper describes the application of a Nonlinear Autoregressive
Neural Network (NARNN) as a novel approach to forecast interference levels in a
wireless system for the purpose of efficient resource allocation. Accurate
interference forecasts also grant the possibility of meeting specific outage
probability requirements in URLLC scenarios. Performance of this proposal is
evaluated upon the basis of NARNN predictions accuracy and system resource
usage. Our proposed approach achieved a promising mean absolute percentage
error of 7.8 % on interference predictions and also reduced the resource usage
in up to 15 % when compared to a recently proposed interference prediction
algorithm.
