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

Date

2020-08-13

Advisors

Journal Title

Journal ISSN

ISSN

2043-9377

Volume Title

Publisher

Emerald Publishing

Type

Article

Peer reviewed

Yes

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 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.

Description

The file attached to this record is the author's final peer reviewed version.

Keywords

Grey prediction, Neural network, Co-training, Topic popularity prediction, Markov chain state transition

Citation

Hong, Y., Zhang Q. and Yang, Y. (2020) Grey theory based BP-NN co-training for dense sequence long-term tendency prediction. Grey Systems: Theory and Application. 11 (2), pp. 327-338

Rights

Research Institute

Institute of Artificial Intelligence (IAI)