摘要
[目的]由于经济、金融、环境和生态等多个领域中时间序列数据规模的持续增长,对其进行预测变得日益复杂,为了提高大规模时间序列的长期预测效率,探索构建模糊信息粒的创新方法,以准确反映数据集大小和趋势信息.[方法]首先,根据模糊拓展原理,研究各种模糊信息粒,包括区间型、三角型和高斯型模糊信息粒的距离定义.随后,结合时间序列片段的中心线段和离散程度信息,引入一类新颖的模糊信息粒.这些粒子可以有效捕捉指定时间范围内时间序列的趋势信息和离散程度,进一步地提出高斯型模糊信息粒距离的函数表达式和几何解释.为了将这些粒子用于时间序列预测,设计一类模糊推理预测系统,该系统可以利用历史数据构造模糊信息粒,并从高斯型模糊信息粒序列中提取模糊推理规则.[结果]高斯型模糊信息粒距离的函数表达式具有简洁的数学表示,可以合理地反映两个高斯模糊信息粒的中心线和离散程度的差异.模糊推理预测系统可以从高斯型模糊信息粒序列中提取有效的规则,实现时间序列的长期预测.实验结果表明,结合线性高斯模糊信息粒与模糊推理系统的预测方法在均方根误差和平均绝对百分比误差方面优于其他数值预测算法和其他模糊信息粒推理方法,包括自回归模型、自回归神经网络和回归向量机等.[结论]结合线性模糊信息粒和模糊推理系统的方法可以提高时间序列长期预测的效率.基于对数据集特征的合理抽象提出了一种新颖的线性模糊信息粒,并简洁地推导出了它们的距离定义.时间序列预测的成功表明,通过巧妙地设计信息粒,能够准确捕捉数据集中的关键特征,从而提高其他数据挖掘任务的效率,例如更快的计算速度和更准确的结果.
[Objective]Our research aims to tackle the prevalent challenge of time series prediction in diverse fields such as economics,finance,environment,and ecology.With the continuous growth in the scale of time series data due to advancements in computer and IoT technologies,predicting these large-scale sequences has become increasingly complex.Primarily,we attempt to explore innovative approaches for constructing fuzzy information granules that accurately reflect both dataset size and trend information.In this study,we provide a robust solution to inherent difficulties in forecasting large-scale time series by enhancing the efficiency of prediction algorithms through the strategic design of these granules.[Methods]The research employs a multi-faceted methodology.Initially,the study establishes comprehensive distance definitions for various fuzzy information granules,including interval,triangular,and Gaussian types,based on the fuzzy extension principles.Subsequently,a novel class of fuzzy information granules is introduced,and the central line segment and dispersion of the dataset are considered.These granules effectively capture the development trends and dispersion characteristics of time series within a specified timeframe.In the study,we further present a functional expression and geometric interpretation for the distance of Gaussian fuzzy information granules.To operationalize these granules for time series prediction,we design a fuzzy inference prediction system,and leverage historical data as well as rules extracted from Gaussian fuzzy information granule distances.[Results]The functional expression for Gaussian fuzzy information granule distance constitutes a concise mathematical representation,allowing for a reasonable interpretation as the amalgamation of disparities in central lines and deviation degrees.Then,the fuzzy inference prediction system,in which Gaussian fuzzy information granule distances is utilized,successfully extracts effective rules from extensive historical data,facilitating long-term predictions for time series.Results emphasize the superiority of the proposed approach in terms of root-mean-square error and mean-absolute-percentage error,thus highlighting its potential for improving the accuracy of long-term time series predictions.Comparative analyses against various numerical prediction algorithms and alternative fuzzy information granule inference methods,including autoregressive models,autoregressive neural networks,and regression vector machines,consistently demonstrate enhanced outcomes achieved by combining linear Gaussian fuzzy information granules with the fuzzy inference system.[Conclusions]Our study provides a comprehensive exploration of challenges inherent in time series prediction and proposes a methodology to address these challenges effectively.The designed fuzzy information granules,informed by meticulous distance definitions and consideration of dataset characteristics,offer results for accurate and efficient long-term time series predictions.Satisfactory results in time series prediction suggest that,by skillfully designing information granules,we can accurately capture key features in the dataset,thereby enhancing the efficiency of other data mining tasks.This outcome includes improvements such as fast computational speed and accurate results.
作者
杨昔阳
陈豪
李志伟
张新军
颜星华
YANG Xiyang;CHEN Hao;LI Zhiwei;ZHANG Xinjun;YAN Xinghua(Fujian Provincial Key Laboratory of Data-Intensive Computing,Quanzhou Normal University,Quanzhou 362000,China;Key Laboratory of Intelligent Computing and Information Processing,Quanzhou Normal University,Quanzhou 362000,China;Fujian Key Laboratory of Financial Information Processing,Putian University,Putian 351100,China)
出处
《厦门大学学报（自然科学版）》
CAS
CSCD
北大核心
2024年第2期188-198,共11页
Journal of Xiamen University：Natural Science
基金
福建省自然科学基金(2021J01001)
泉州市科技局资助项目(2021N042)
福建省教育厅2020年度福建省中青年教师教育科研项目(科技类)(JAT200566)
福建省金融信息处理重点实验室(莆田学院)开放课题(JXC202205)。
关键词
线性模糊信息粒
模糊推理系统
时间序列预测
linear fuzzy information granule
fuzzy inference system
time series prediction
