通过模糊推理系统进行时间序列预测的工具

F. Montesino, A. Lendasse, A. Barriga
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引用次数: 6

摘要

本文报道了一种利用模糊推理系统进行时间序列预测的新软件工具。这个名为xftsp的工具实现了一种新的时间序列预测方法,该方法基于自动模糊系统识别方法和监督学习方法,结合非参数残差估计的统计方法。xftsp是一个集成在Xfuzzy开发环境中的模糊系统开发工具。在许多时间序列基准上进行的实验表明,与时间序列预测领域的一种成熟技术——最小二乘支持向量机(Least-Squared Support Vector Machines)相比,xftsp在准确性和计算需求方面都具有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
xftsp: A tool for time series prediction by means of fuzzy inference systems
A new software tool for time series prediction by means of fuzzy inference systems is reported. This tool, named xftsp, implements a novel methodology for time series prediction based on methods for automatic fuzzy systems identification and supervised learning combined with statistical methods for nonparametric residual variance estimation. xftsp is designed as a tool integrated in the Xfuzzy development environment for fuzzy systems. Experiments carried out on a number of time series benchmarks show the advantages of xftsp in terms of both accuracy and computational requirements as compared against Least-Squared Support Vector Machines, an established technique in the field of time series prediction.
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