Grey theory–based BP-NN co-training for dense sequence long-term tendency prediction. Issue 2 (13th August 2020)

• Record Type: Journal Article
• Authors: Hong, Yuling; Yang, Yingjie; Zhang, Qishan
• Is Part Of: Grey systems. Volume 11:Issue 2(2021)
• Page Start: 327
• Page End: 338
• Publication Date: 2020-08-13
• Subjects: Grey prediction -- Neural network -- Co-training -- Topic popularity prediction -- Markov chain state transition; Cybernetics -- Periodicals; Systems engineering -- Periodicals; 003.5
• DOI: 10.1108/GS-02-2020-0024
• Languages: English
• ISSNs: 2043-9377

Abstract

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.