Online public opinion prediction based on rolling fractional grey model with new information priority

The data of online public opinion updates quickly and new information plays a greater role than old information, this paper proposes a novel new information variable weight fractional rolling grey model for predicting online public opinion trends. Firstly, the new information variable weight buffer operator is proposed, and it follows the principle of New information priority. The new information is assigned larger weight under the action of the buffer operator. On the basis of the buffer operator, the new information variable weight fractional order accumulating generated operator is proposed, which has the dual functions of accumulation and weighting. In addition, the idea of metabolism is introduced to the model, which adds the latest online public opinion time point data and eliminates the earliest data. Finally, this model is applied to forecast the online public opinion trends with monotonically increasing sequence, monotonically decreasing sequence, and oscillation sequence. Compared with other models, the new information variable-weight fractional rolling grey model has better prediction performance in the online public opinion.

Automated summary

This paper proposes a novel new information variable weight fractional rolling grey model for predicting online public opinion trends, which shows better prediction performance compared with other models.