基于改进狼群算法的模糊时间序列预测模型

当使用模糊时间序列预测模型进行预测时, 模糊区间的不同划分对最后的预测精度有着十分重要的影响. 针对如何更有效的划分模糊区间、进一步提高模糊时间序列的预测精度问题, 本文提出了一种基于改进狼群算法 的模糊时间序列预测模型. 为此首先简要介绍了模糊时间序列, 然后阐述了狼群算法并在其游走行为中引入趋向 行为和死亡概率对其进行了改进, 最后利用改进狼群算法来划分模糊区间, 建立了一种新的模糊时间序列预测模 型. 将Alabama大学入学人数作为实验数据进行实例分析和验证. 通过与现有的一些模型进行对比分析, 本文所提 模型具有更高的预测精度, 为模糊时间序列预测提供了新思路.

Fuzzy time series prediction model based on improved wolf pack algorithm

The division of fuzzy interval plays a very important role to the final prediction accuracy when one uses the fuzzy time series prediction model to prediction. In order to solve the problem of how to divide fuzzy interval more effectively and further improve the prediction accuracy, a fuzzy time series prediction model based on improved wolf pack algorithm is proposed in this paper. Firstly, the fuzzy time series is briefly introduced, then the wolf pack algorithm is described and the chemotactic behavior and death probability are introduced into its scouting behavior to improve it. Finally, using the improved wolf pack algorithm to divide the fuzzy interval, a new fuzzy time series prediction model is established. The enrollments of university of Alabama is analyzed and verified as experimental data. By comparing the forecasting results with some existing models, the proposed model has higher prediction accuracy and provides a new idea for fuzzy time series prediction.