Time series data analysis and forecasting tool for studying the data on the use of network traffic is very important to provide acceptable and good quality network services, including network monitoring, resource management, and threat detection. More and more, the behavior of network traffic is described by the theory of deterministic chaos. The traffic of a modern network has a complex structure, an uneven rate of packet arrival for service by network devices. Predicting network traffic is still an important task, as forecast data provide the necessary information to solve the problem of managing network flows. Numerous studies of actually measured data confirm that they are nonstationary and their structure is multicomponent. This paper presents modeling using Nonlinear Autoregression Exogenous (NARX) algorithm for predicting network traffic datasets. NARX is one of the models that can be used to demonstrate non-linear systems, especially in modeling time series datasets. In other words, they called the categories of dynamic feedback networks covering several layers of the network. An artificial neural network (ANN) was developed, trained and tested using the LM learning algorithm (Levenberg-Macwardt). The initial data for the prediction is the actual measured network traffic of the packet rate. As a result of the study of the initial data, the best value of the smallest mean-square error MSE (Mean Squared Error) was obtained with the epoch value equal to 18. As for the regression R, its output ANN values in relation to the target for training, validation and testing were 0.97743. 0.9638 and 0.94907, respectively, with an overall regression value of 0.97134, which ensures that all datasets match exactly. Experimental results (MSE, R) have proven the method's ability to accurately estimate and predict network traffic

Nonlinear optical effects in optical waveguides play an important role in the development of fiber and integrated optics systems for optical communication and information processing. On the one hand, nonlinear effects impose restrictions on the radiation power that can be transmitted through an optical fiber or light guide. In this paper, the problem of the occurrence of the phenomenon from the stimulated Brillouin scattering (SBS) effect is investigated using two optical sources of rays in a single-mode optical fiber at joint waves of 1310 nm and 1550 nm. Due to the fact that in all trunk fiber-optic lines, the intensity and energy of input signals is limited due to the influence of SBS, methods are currently being sought to reduce the influence of this phenomenon. It was found that the energy of the input beam in the combined propagation of the compound did not reach the value of the threshold of SBS in the values of 25 dBm and 27 dBm due to the discrepancy between the experimental results and the results of the model. The SBS effect was not observed when the threshold threshold of 15 dBm and 27 dBm – SBS was reached when combining a dual beam along a single optical fiber in one direction. As a result, by double integration, the value of the SBS threshold was raised, and the direction for future scientific research was determined. If the possibility of increasing the threshold of SBS is proved, then increasing the distance of amplifiers in the main networks, respectively, its economic effect increases. In addition, it can be noted that there are no scientific papers devoted to the study of the effects of optical nonlinear effects by combining and distributing these two compounds along a single optical fiber. This article discusses the issues of improving the capacity and determination of the threshold for stimulated Brillouin scattering. To increase the power threshold is invited to consider the dependence of the phase modulation frequency of the spectral width of the laser radiation. In addition, the fact that the SBS threshold did not reach the value at an energy of 27 DB in the case of the double-beam distribution can be proved by discrepancies in comparison with the results of experimental studies and the results of the model that determines the SBS effect