Abstract
The paper considers a problem of forecasting of news feeds content. Analysis of existing approaches to this problem solution reveals the need for development of methods with enhanced forecasting capabilities. A method is proposed with expanded accounting for space and time relationships of the processed data. The method is revealed through an example of a neural network forecasting system that implements it. Implementation includes data retrieval from news feeds, their special preprocessing, coding and forecasting of words sets and their interconnections, followed by highlighting news topics and describing the of news feeds content. Some variants of stream recurrent neural networks with spiral layer structures were investigated with due regard to their forecasting capabilities under direction and strength control of the associative call of signals from the network memory. The paper also presents and discusses experimental results, a description of the methodological contribution and recommendations on the method practical application.
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