Time-Series Forecasting via Fuzzy-Probabilistic Approach With Evolving Clustering-Based Granulation

Abstract

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.