  Effective resource allocation is a crucial requirement to achieve the
stringent performance targets of ultra-reliable low-latency communication
(URLLC) services. Predicting future interference and utilizing it to design
efficient interference management algorithms is one way to allocate resources
for URLLC services effectively. This paper proposes an empirical mode
decomposition (EMD) based hybrid prediction method to predict the interference
and allocate resources for downlink based on the prediction results. EMD is
used to decompose the past interference values faced by the user equipment.
Long short-term memory and auto-regressive integrated moving average methods
are used to predict the decomposed components. The final predicted interference
value is reconstructed using individual predicted values of decomposed
components. It is found that such a decomposition-based prediction method
reduces the root mean squared error of the prediction by $20 - 25\%$. The
proposed resource allocation algorithm utilizing the EMD-based interference
prediction was found to meet near-optimal allocation of resources and
correspondingly results in $2-3$ orders of magnitude lower outage compared to
state-of-the-art baseline prediction algorithm-based resource allocation.
