ИСТИНА

The results of the research of quality improvement of medium-term data forecasting with neural networks within introduction of statistical models for observations are demonstrated. The main goal is to study the efficiency of nontrivial expansion of the feature space based on the characteristics of finite mixtures models. Such type of probability models are successfully used as convenient approximations of the processes in the plasma turbulence. In the paper, expectation, variance, skewness and kurtosis of the mixture models are introduced as additional features for machine learning algorithms. Comparison of medium-term forecasts with and without additional features is carried out, and various neural network architectures are investigated. The proposed methods are tested on the unique turbulent plasma ensembles obtained from the L-2M stellarator. It is demonstrated that the usage of the above-mentioned statistical characteristics can increase the accuracy of neural network forecasts in terms of such standard metrics as root-mean-square error and mean absolute errors. Hybrid high-performance computing cluster is used in order to increase the learning rate.