Abstract. For a description of the information space it is introduced a vector representation of the constituent text documents that are bound by the events described in the timeline. The predicted event is also represented by a vector obtained on the base of its text description. The mean value of projections of the information space in the direction of the vector of predicted events at different time points is considered as a set of information system states. It is also entered the change values of states. To describe transitions between states is used a probabilistic approach and the difference transition scheme. This makes it possible to get the dependence of the time for the value of the probability density for the event "detection information system in a state" in the form of a second order differential equation. On the basis of this equation is formulated and solved the boundary problem. Carried out by the authors the analysis of the stochastic dynamics of achievement a threshold of realization of news events has allowed the establishing of the ability to increase the probability of transition almost simultaneously with the beginning of the process of the news cluster structure changing. This is due to the presence of the memory of previous states in the information system and the possibility of self-description, as a result of accounting in the differential model information processes on the basis of the second derivative over time. In addition, the proposed model demonstrates the possibility of sudden changes in the probability of crossing the threshold of events and takes into account the presence of oscillations in her behavior. Based on the model developed it is proposed the algorithm for analysis of news clusters relationship in the information field with the possibility of occurrence of the predicted event, and determined the possible time of its implementation.

On the basis of the diffusion theory, we suggested a model for forecasting event in news feeds, which is based on the use of stochastic dynamics of changes in the structure of non-stationary time series in news text clusters (states of the information space). Forecasting events in a news feed is based on their text description, vectorization, and finding the cosine value of the angle between the given vector and the centroids of various information space semantic clusters. Changes over time in the cosine value of such angle between the above vector and centroids can be represented as a point wandering on [0,1] segment. This segment contains a trap at the event occurrence threshold point. The wandering point can fall into this trap over time. We have considered probability patterns of transitions between states in the information space. We have derived a nonlinear second-order differential equation; formulated and solved the boundary value problem of forecasting news events. We have obtained theoretical time dependence for the probability density function of the parameter distribution of non-stationary time series that describe the information space evolution. The results of simulating the time dependence of the event probability (with sets of parameter values of the developed model, which have been experimentally determined for already occurred events) show that the model is consistent and adequate. Experimental verification of the proposed model was carried out using a corpus of texts written in Russian.