This article studies the predictive abilities of the generative-adversarial neural network approach in relation to time series using the example of price forecasting for the nodes of the Russian free electricity market for the day ahead. As a result of a series of experiments, we came to the conclusion that a generative adversarial network, consisting of two models (generator and discriminator), allows one to achieve a minimum of the error function with a greater generalizing ability than, all other things being equal, is achieved as a result of optimizing the static analogue of the generative model – recurrent neural network. Our own empirical results show that with a near-zero mean square error on the training set, which is demonstrated simultaneously by the recurrent and generative models, the error of the latter on the test set is lower. The adversarial approach also outperformed alternative reference models in out-of-sample forecasting accuracy: a convolutional neural network adapted for time series forecasting and an autoregressive linear model. Application of the proposed approach has shown that a generative-adversarial model with a given universal architecture and a limited number of explanatory factors, subject to additional training on data specific to the target node of the power system, can be used to predict prices in market nodes for the day ahead without significant deviations.