Abstract. The deployment of emerging cloud computing technologies onto telecommunication (telco) infrastructures, coupled with highly stringent regulations relating to energy efficiency, power consumption, and CO2 emissions, put telco datacenters and their associated operations under a drastic transformation process. This paves the way for new optimization opportunities, such as dynamic datacenter sizing (DDS) with respect to power consumption constraints and volatile demands across multiple resource types. DDS boils down to determining the optimal subset of active servers in a datacenter, and it can be modeled as a planning problem where decisions regarding resource availability are based on projections of resource demands. This raises the need for accurate and efficient forecasting solutions, which shall be tailored to the problem at hand.To this end, our work focuses on the investigation, the development, and the evaluation of forecasting methods which predict the demand of 5G (and beyond) workloads across multiple resources. Concretely, using the daily pattern of load data pertaining to an operational data-plane function and the prevailing way of horizontal scaling in virtualized datacenters, we exemplify the evolution of resource demands of 5G applications residing at the network edge. This lets us simulate a dataset of demands across various resource types, upon which we study informative features, and then train and evaluate a wide suite of forecasting methods. Our experiments show that the intrinsic characteristics of the data render simple statistical algorithms capable of accurately capturing the underlying patterns, and even outperforming more complex algorithms. Last, we evaluate and discuss the trade-off between single- and multi-output forecasting models.