Time-series prediction based on information granule in which the algorithm is developed by deriving the relations existing in the granular time series, has achieved excellent success. However, the existing uncertainty in data and the computational demand of the granulation process make it difficult for these methods to accurately and efficiently achieve long-term prediction. In this article, a fuzzy-probabilistic prediction approach with evolving clustering-based granulation is proposed. First, the evolving clustering-based granulation strategy is proposed to transform the original numerical data into information granules. The granulation process is performed in an incremental way and the information granules are represented with the triplets, which can efficiently reduce the computation overhead. Then, the proposed information granule clustering is used to derive the group relations in the information granules. Based on the logical relationships of information granules in the temporal order, the information granule forecasting the integrated fuzzy and probability theory is proposed to deal with uncertainties and perform final long-term prediction. A series of experiments using publicly available time series are conducted, and the comparative analysis demonstrates that the proposed approach can achieve a better performance for regular and Big Data time series than the existing granular and numeric models for long-term prediction.

中文翻译：

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通过基于进化聚类的模糊概率方法进行时间序列预测

基于信息颗粒的时间序列预测，通过推导颗粒时间序列中存在的关系来开发算法，取得了巨大的成功。然而，数据中存在的不确定性和造粒过程的计算需求使得这些方法难以准确有效地实现长期预测。在这篇文章中，提出了一种具有进化的基于聚类的粒度的模糊概率预测方法。首先，提出了基于进化聚类的粒化策略，将原始数值数据转化为信息粒。粒化过程以增量方式进行，信息粒用三元组表示，可以有效降低计算开销。然后，所提出的信息颗粒聚类用于导出信息颗粒中的组关系。基于时间顺序信息颗粒的逻辑关系，提出了综合模糊和概率理论的信息颗粒预测，以处理不确定性并进行最终的长期预测。使用公开可用的时间序列进行了一系列实验，比较分析表明，与用于长期预测的现有粒度和数值模型相比，所提出的方法可以为常规和大数据时间序列实现更好的性能。提出了综合模糊和概率理论的信息粒预测来处理不确定性并进行最终的长期预测。使用公开可用的时间序列进行了一系列实验，比较分析表明，与用于长期预测的现有粒度和数值模型相比，所提出的方法可以为常规和大数据时间序列实现更好的性能。提出了综合模糊和概率理论的信息粒预测来处理不确定性并进行最终的长期预测。使用公开可用的时间序列进行了一系列实验，比较分析表明，与用于长期预测的现有粒度和数值模型相比，所提出的方法可以为常规和大数据时间序列实现更好的性能。