In telecommunications, time-division signals play an extremely important role in the process of information transmission and processing. These signals, which are presented as electromagnetic waves, serve as the main means of transport for the transmission of data from one communication device to another, effectively constituting the circulatory system of modern communication networks. The defining feature that distinguishes these signals is their temporal nature, or more precisely, the changes they undergo over time as they travel over the network. These signals contain a rich spectrum of parameters, covering not only the attributes of amplitude, frequency and phase, but also many other parameters. This article provides a scientific rationale for the feasibility and relevance of researching the process of forming signal ensembles with temporal separation using neural networks. It has been established that one of the key tasks in creating ensembles of complex signals with temporal separation through the application of neural networks is the selection of an optimal neural network architecture. This entails determining the type of neural network that best aligns with a specific research objective. An algorithm for forming signal ensembles with temporal separation using a recurrent neural network (RNN) has been developed. Special attention is devoted to methods for assessing the overall error of signal ensembles with temporal separation using PNN. These analytical methods encompass the utilization of pertinent metrics, the evaluation of which depends on the specific nature of the task, whether it involves regression or classification. The findings of this research will contribute to the further advancement of methods for forming signal ensembles with temporal separation through the utilization of neural networks and enhance their application in contemporary telecommunications systems.