Grey system theory-based models in time series prediction
Grey system theory-based models in time series prediction
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DOI：
10.1016/j.eswa.2009.07.064
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发表时间：
2010-03-01
影响因子：
8.5
通讯作者：
Kaynak, Okyay
中科院分区：
文献类型：
--
作者：
Kayacan, Erdal;Ulutas, Baris;Kaynak, Okyay
能够准确地预测时间序列一直是研究人员的热门课题，无论是过去还是现在。然而，传统的分析方法缺乏预测能力的时间序列，也不顺利导致科学家和研究人员诉诸各种预测模型，具有不同的数学背景，如人工神经网络，模糊预测器，进化和遗传算法。在本文中。研究了GM（1，1）模型、灰色Verhulst模型、基于傅立叶级数的修正灰色模型的精度。以2005年1月1日至2007年12月30日的美元对欧元比价数据为例，比较了不同模型的性能。仿真结果表明，改进的灰色模型不仅在模型拟合方面，而且在预测方面都有较好的性能。在这些灰色模型中，采用时间傅立叶级数的修正GM（1.1）模型拟合和预测效果最好。(C)2009爱思唯尔有限公司版权所有
Being able to forecast time series accurately has been quite a popular subject for researchers both in the past and at present. However, the lack of ability of conventional analysis methods to forecast time series that are nor smooth leads the scientists and researchers to resort to various forecasting models that have different mathematical backgrounds, such as artificial neural networks, fuzzy predictors, evolutionary and genetic algorithms. In this paper. the accuracies of different grey models such as GM(1,1), Grey Verhulst model, modified grey models using Fourier Series is investigated. Highly noisy data, the United States dollar to Euro parity between the dates 01.01.2005 and 30.12.2007, are used to compare the performances of the different models The simulation results show that modified grey models have higher performances not only on model fitting but also on forecasting. Among these grey models, the modified GM(1.1) using Fourier series in time is the best in model fitting and forecasting. (C) 2009 Elsevier Ltd. All rights reserved