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论文类型：期刊论文

发表时间：2015-05-01

发表刊物：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录刊物：SCIE、EI

卷号：41

页面范围：17-24

ISSN号：0952-1976

关键字：Information granules; Granular time series; Forecasting; Long-term forecasting; Dynamic time warping

摘要：In spite of the impressive diversity of models of time series, there is still an acute need to develop constructs that are both accurate and transparent. Meanwhile, long-term time series prediction is challenging and of great interest to both practitioners and research community. The role of information granulation is to organize detailed numerical data into some meaningful, semantically sound entities. With this regard, the design of time series forecasting models used the information granulation is interpretable and easily comprehended by humans. In order to cluster information granules, a modified fuzzy c-means which does not require that data have the same dimensionality is proposed. Then, we develop forecasting model combining the modified fuzzy c-means and information granulation for solving the problem of time series long-term prediction. Synthetic time series, chaotic Mackey-Glass time series, power demand, daily temperatures, stock index, and wind speed are used in a series of experiments. The experimental results show that the proposed model produces better forecasting results than several existing models. (C) 2015 Elsevier Ltd. All rights reserved.