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

Abstract

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 co-training 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.

Citation

Hong, Y., Yang, Y. and Zhang, Q. (2021), "Grey theory–based BP-NN co-training for dense sequence long-term tendency prediction", Grey Systems: Theory and Application, Vol. 11 No. 2, pp. 327-338. https://doi.org/10.1108/GS-02-2020-0024