Multi-factor high-order intuitionistic fuzzy timeseries forecasting model

Fuzzy sets theory cannot describe the neutrality degreeof data, which has largely limited the objectivity of fuzzy time seriesin uncertain data forecasting. With this regard, a multi-factor highorderintuitionistic fuzzy time series forecasting model is built. Inthe new model, a fuzzy clustering algorithm is used to get unequalintervals, and a more objective technique for ascertaining membershipand non-membership functions of the intuitionistic fuzzy setis proposed. On these bases, forecast rules based on multidimensionalintuitionistic fuzzy modus ponens inference are established.Finally, contrast experiments on the daily mean temperature ofBeijing are carried out, which show that the novel model has aclear advantage of improving the forecast accuracy.

Adaptive partition intuitionistic fuzzy time series forecasting model

To enhance the accuracy of intuitionistic fuzzy time series forecasting model, this paper analyses the influence of universe of discourse partition and compares with relevant literature. Traditional models usually partition the global universe of discourse, which is not appropriate for all objectives. For example, the universe of the secular trend model is continuously variational. In addition, most forecasting methods rely on prior information, i.e., fuzzy relationship groups (FRG). Numerous relationship groups lead to the explosive growth of relationship library in a linear model and increase the computational complexity. To overcome problems above and ascertain an appropriate order, an intuitionistic fuzzy time series forecasting model based on order decision and adaptive partition algorithm is proposed. By forecasting the vector operator matrix, the proposed model can adjust partitions and intervals adaptively. The proposed model is tested on student enrollments of Alabama dataset, typical seasonal dataset Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and a secular trend dataset of total retail sales for social consumer goods in China. Experimental results illustrate the validity and applicability of the proposed method for different patterns of dataset.

Assessment and sequencing of air target threat based on intuitionistic fuzzy entropy and dynamic VIKOR

In view of the fact that traditional air target threat assessment methods are difficult to reflect the combat characteristics of uncertain, dynamic and hybrid formation, an algorithm is proposed to solve the multi-target threat assessment problems. The target attribute weight is calculated by the intuitionistic fuzzy entropy(IFE) algorithm and the time series weight is gained by the Poisson distribution method based on multi-times data. Finally,assessment and sequencing of the air multi-target threat model based on IFE and dynamic Vlse Kriterijumska Optimizacija I Kompromisno Resenje(VIKOR) is established with an example which indicates that the method is reasonable and effective.

A fuzzy time series forecasting method induced by intuitionistic fuzzy sets

Intuitionistic fuzzy sets(IFSs)are well established as a tool to handle the hesitation in the decision system.In this research paper,fuzzy sets induced by IFS are used to develop a fuzzy time series forecasting model to incorporate degree of hesitation(nondeterminacy).To improve the forecasting accuracy,induced fuzzy sets are used to establish fuzzy logical relations.To verify the performance of the proposed model,it is implemented on one of the benchmarking time series data.Further,developed forecasting method is also tested and validated by applying it on a financial time series data.In order to show the accuracy in forecasting,the method is compared with other forecasting methods using different error measures.

Handling higher order time series forecasting approach in intuitionistic fuzzy environment

This study presents a new method of forecasting based on a higher order intuitionistic fuzzy time series(FTS)by transforming FTS data into intuitionistic FTS data via defining their appropriate membership and non-membership grades.The fuzzification of time series data is intuitionistic fuzzification which is based on the maximum score degree of intuitionistic fuzzy numbers.Also,the intuitionistic fuzzy logical relationship groups are defined and introduced into a defuzzification process for a higher order intuitionistic FTS that enhances in the forecasted output.In order to assess the performance of the proposed method,the method has been implemented on the historical data of rice production.The comparison result shows that the proposed method can achieve a better forecasting accuracy rate in terms of RMSE and MAPE than the existing methods such as Song and Chissom[(1993).Forecasting enrolments with fuzzy time series-Part I.Fuzzy Sets and System,54,1-9],Chen[(1996).Forecasting enrolments based on fuzzy time series.Fuzzy Sets and System,81,311-319],Singh[(2007a).A simple method of forecasting based on fuzzy time series.Applied Mathematics and Computation,186,330-339]and Abhishekh,Gautam,and Singh[(2017).A refined weighted method for forecasting based on type 2 fuzzy time series.International Journal of ModellingandSimulation,38,180-188;(2018).A score function based method of forecasting using intuitionistic fuzzy time series.New Mathematics and Natural Computation,14(1),91-111].

基于矢量量化IFTS的网络流量预测模型

针对传统网络流量预测模型存在的局限性,提出一种基于矢量量化直觉模糊时间序列的网络流量预测模型。利用模糊直觉推理有效地表述了网络流量数据中存在的高度模糊性以及不确定性,利用直觉模糊时间序列矢量距离作为评估标准,并且通过坐标平移与质心进行匹配,提升不同时间序列段的分类能力,从而有效地建立网络流量预测模型。通过实验分析可知,提出的预测模型能够提升预测精度并且减少计算复杂度,另外该算法有能力长期预测多个输出。

基于IFE动态直觉模糊法的空战目标威胁评估

针对传统空战多目标威胁评估难以与动态作战态势相结合的缺陷和不足,提出了基于直觉模糊熵(intuitionistic fuzzy entropy,IFE)与动态直觉模糊法相结合的空战多目标威胁评估方法。该方法通过直觉模糊集确定目标属性矩阵,采用IFE算法对目标属性赋权;采用泊松分布法处理多时刻态势信息,计算时间序列权重;建立动态直觉模糊空战多目标威胁评估数学模型,并进行了仿真研究。仿真结果表明该算法是合理和有效的。

基于直觉模糊推理的直觉模糊时间序列模型

由于受到模糊集理论的限制,模糊时间序列预测理论在不确定数据集的描述上有失客观,针对这种局限性,提出一种直觉模糊时间序列预测模型。应用模糊聚类算法实现论域的非等分划分;针对直觉模糊时间序列的数据特性,提出一种更具客观性的隶属度和非隶属度函数的确定方法;提出一种基于直觉模糊近似推理的模型预测规则。在Alabama大学入学人数和中国社会消费品零售总额数据集两组数据集上分别与典型方法进行对比实验,结果表明该模型有效提高了预测精度,证明了模型的有效性和优越性。

基于直觉模糊集的空战动态多属性威胁评估

针对实际空战中目标多属性及动态变化的特点,提出了一种动态多属性威胁评估方法。在建立威胁评估指标体系的基础上,依据目标多个时刻的属性信息,结合直觉模糊集给出了一种构造虚拟最优目标的方法以确定目标属性权重,设计了一种基于正态分布累积分布函数确定时间序列权重的方法,最后借鉴TOPSIS的思想实现了空战动态威胁排序。仿真结果说明了该算法的合理性。

高阶直觉模糊时间序列预测模型

提出一种高阶直觉模糊时间序列预测模型。模型首先应用模糊聚类算法实现论域的非等分划分;然后,针对直觉模糊时间序列的数据特性,提出一种更具客观性的直觉模糊集隶属度和非隶属度函数的确定方法;最后,利用直觉模糊多维取式推理建立高阶模型的预测规则,进行预测。在Alabama大学入学人数和北京市日均气温2组数据集上分别与典型方法进行对比实验,结果表明该模型有效提高了预测精度,证明了模型的有效性和优越性。

基于DTW的长期直觉模糊时间序列预测模型

针对现有直觉模糊时间序列模型中直觉模糊关系组和确定性转换规则过度依赖训练数据规模的问题,提出一种基于动态时间弯曲(DTW,dynamic time warping)距离的长期直觉模糊时间序列预测模型。通过直觉模糊C均值(IFCM,intuitionistic fuzzy C mean)聚类构建直觉模糊时间序列片段库,动态更新和维护规则库,减少系统复杂度。提出基于DTW距离的直觉模糊时间序列片段相似度计算方法,有效解决不等长时间序列片段匹配问题。通过对合成数据以及包含不同时间序列模式的气温数据的实验,与其他相关模型比较,说明该模型对于不同时间序列趋势变化模式中均具有较高的预测能力,克服传统模型提高模型只能满足单一模式时间序列预测,提高模型的泛化性能。

基于确定性转换的IFTS预测

模糊时间序列预测模型在对不确定数据集的模糊趋势描述和论域划分方面有局限性,对此文中规范了直觉模糊时间序列的定义,应用直觉模糊C均值聚类算法优化论域区间划分,通过加入回溯机制构建确定性转换直觉模糊规则库,在此基础上提出一种直觉模糊时间序列预测方法,较好地反映了不确定系统数据的特征分布,提高了复杂环境下时间序列的预测精度.通过典型实例验证了该方法的有效性和优越性.

基于矢量量化的长期直觉模糊时间序列预测

通过引入滑动窗口机制和矢量量化技术,较好地解决了直觉模糊规则零匹配的问题,准确反映了不确定时序系统数据的分布特性,提高了复杂环境下时间序列长期趋势预测的精度,扩展了直觉模糊时间序列预测理论的应用范围。最后通过典型实例验证了该方法的有效性和优越性。

基于UKF的自组织直觉模糊神经网络

模糊集在语义描述上存在不足,因此,如何对模糊神经网络进行扩展是当前模糊神经网络研究的热点,针对这一问题,本文提出了基于UKF的自组织直觉模糊神经网络.首先,给出了直觉模糊神经网络的结构和各层的含义;其次,推导了直觉模糊神经网络的学习算法,用LLS和UKF分别学习线性和非线性参数;然后,给出了模糊规则生成的准则,并用误差下降率方法作为规则修剪的策略,删除作用不大的规则;最后,通过典型的函数逼近、系统辨识和时间序列预测实例,表明本文算法得到的直觉模糊神经网络的结构更为紧凑,泛化性能也更佳.

一种启发式变阶直觉模糊时间序列预测模型

论文针对已有高阶模糊时间序列模型在预测精度和预测范围上的限制,结合直觉模糊集理论,提出一种启发式变阶直觉模糊时间序列预测模型。模型首先应用直接模糊聚类算法对论域进行非等分划分;然后,针对直觉模糊时间序列的数据特性,改进现有直觉模糊集隶属度和非隶属度函数的建立方法;最后,采用阶数随序列实时变化的高阶预测规则进行预测,并将历史数据发展趋势的启发知识引入解模糊过程,使模型的预测范围得到扩展。在Alabama大学入学人数和北京市日均气温两组数据集上分别与典型方法进行对比实验,结果表明该模型有效克服了传统模型的缺点,拥有较高的预测精度,证明了模型的有效性和优越性。

高阶多元直觉模糊时间序列预测模型

为了突破模糊集理论的限制,更客观地描述不确定性数据,提出一种高阶多元直觉模糊时间序列预测模型.采用模糊聚类算法划分论域,并采用更具客观性的方法建立直觉模糊集的隶属度和非隶属度函数.依据直觉模糊多维取式推理的原理建立基于相似度量的启发式推理规则,作为高阶多元模型的预测规则,并且建立相应的解模糊方法.利用北京市日均气温数据集进行对比实验,结果表明,该模型的预测均方误差(0.86)和平均预测误差(2.57%)较现有方法均明显降低,预测结果优于模糊时间序列预测模型和普通直觉模糊时间序列预测模型.

基于直觉时间模糊序列的上市公司财务风险预测研究

针对上市公司进行企业财务风险预测,以盈利能力、营运能力、成长能力、偿债能力4个角度等相关财务数据指标建立了直觉模糊预测模型。通过选取财务风险评价指标,利用数据固有的模糊性特征,建立财务风险直觉模糊预测模型,并通过不同参数因子获取不同聚类参数进行多次预测,以提高预测准确性。选取上市公司近8年财务数据,构建财务风险直觉模糊预测模型,通过验证表明,此模型能够充分考虑到固有数据的模糊性,提高了预测准确性,并与其他预测模型对比,将误差降低到了3.92%,从而企业可以根据历史财务信息有效开展财务风险预测工作,以便能够为企业日常运营中的战略方向、经营决策以及资源提供了可靠依据。

基于直觉模糊多属性决策的动态威胁评估模型

针对防空作战中目标多属性及其属性值动态变化的特点,提出了一种基于直觉模糊多属性决策的动态威胁评估方法。首先,针对目标属性权重未知的情况,在综合考查决策者主观偏好和客观信息的基础上,建立了线性加权的属性权重优化模型,确定了目标属性权重;其次,针对传统威胁评估算法仅研究当前时刻属性信息的片面性,运用了泊松分布法对时间序列赋权,实现了多时刻目标信息的融合,考查了作战态势的动态变化过程;然后,采用TOPSIS的思想求解目标归一化的威胁值,从而得到来袭目标的动态威胁排序结果;最后,通过仿真实例验证了算法的灵活性和合理性,为防空作战决策者提供更加有效的辅助决策。

一种新的直觉模糊时间序列预测方法

针对现有直觉模糊时间序列预测模型论域区间划分和序列数据直觉模糊化预处理方法存在的问题,提出了一种新的直觉模糊时间序列预测算法,通过引入滑动窗口参数准确反映不确定数据集的分布特性,利用可调参的直觉模糊C均值聚类算法优化论域区间划分标准,基于直觉模糊范数定义语言变量直觉模糊集,有效地提高了复杂环境下时序系统的预测精度。最后,通过典型实例验证了该方法的有效性和优越性。

直觉模糊时间序列

针对模糊时间序列模型在处理数样本数据时存在局限性,引入直觉模糊集理论对模糊时间序列模型进行扩展。首先,通过对样本数据直觉模糊化,更加细腻地反映实际数据的不确定性本质;然后,在建立直觉模糊逻辑关系时引入犹豫度因子,更加真实地描述数据之间状态转换的不确定性,以Song、Chen和Lee提出的模型为框架,构建直觉模糊时间序列预测模型;最后,利用Alabama大学22年的入学人数为实验数据,对比分析Song、Chen和Lee模型及其加权模型的预测结果,验证了直觉模糊时间序列模型的可行性和优越性。