Grey theory based BP-NN co-training for dense sequence long-term tendency prediction

Purpose - The purpose of this paper is to solve the problems existing in topic popularity prediction in online social networks and advance a fine-grained and long-term prediction model for lack of sufficient data. Design/methodology/approach - Based on GM(1,1) and neural networks, a cotraining model for topic tendency prediction is proposed in this paper. The interpolation based on GM(1,1) is employed to generate fine-grained prediction values of topic popularity time series and two neural network models are considered to achieve convergence by transmitting training parameters via their loss functions. Findings - The experiment results indicate that the integrated model can effectively predict dense sequence with higher performance than other algorithms, such as NN and RBF_LSSVM. Furthermore, the Markov chain state transition probability matrix model is used to improve the prediction results. Practical implications - Fine-grained and long-term topic popularity prediction, further improvement could be made by predicting any interpolation in the time interval of popularity data points. Originality/value - The paper succeeds in constructing a co-training model with GM(1,1) and neural networks. Markov chain state transition probability matrix is deployed for further improvement of popularity tendency prediction.