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Strongα-cut and associated membership-based modeling for Fuzzy time series forecasting

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第1期12卷 53-72页

作者： Gunjan Goyal Dinesh C.S.Bisht Department of Mathematics Jaypee Institute of Information Technology NoidaIndia

In this paper,a method is proposed to deal with factors affecting the Fuzzy time series forecasting.A new fuzzification process is used by considering all the fuzzy sets with nonzero membership values corresponding to the data points.A strong alpha-cut based method is presented to select appropriate fuzzy logical relationships that carry importance in analyzing the trend of time series.Further,a unique defuzzification approach based on weights is proposed to get crisp variation.This obtained variation is finally converted to the forecasted value.The presented method is tested on the benchmark enrolment dataset of Alabama University and seven datasets of the Taiwan Capitalization Weighted Stock Index.On comparing the results,it is observed that the presented method performs better than the existing methods.Also,the statistical measures indicate the good forecasting results of the presented method.

关键词： Associated membership grade forecasting fuzzy logical relationship Fuzzy time series strongα-cut

Integrated parallel forecasting model based on modified Fuzzy time series and SVM

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Journal of Systems Engineering and Electronics 2017年第4期28卷 766-775页

作者： Yong Shuai Tailiang Song Jianping Wang Technical Support Engineering Faculty Academy of Armored Forced Engineering Beijing 100072 China Unit 68207 of the PLA Jiayuguan 735100 China China Defense Science & Technology Information Center Beijing 100142 China

A dynamic parallel forecasting model is proposed, which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly, the input data sets are optimized and their coherence is ensured, the region scale algorithm is modified and non-isometric multi scale region Fuzzy time series model is built. At the same time, the particle swarm optimization algorithm about the particle speed, location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate.

关键词： fuzzy C-means clustering Fuzzy time series interval partitioning support vector machine particle swarm optimization algorithm parallel forecasting

Research on Short-Term Load Forecasting of Distribution Stations Based on the Clustering Improvement Fuzzy time series Algorithm

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2221-2236页

作者： Jipeng Gu Weijie Zhang Youbing Zhang Binjie Wang Wei Lou Mingkang Ye Linhai Wang Tao Liu College of Information Engineering Zhejiang University of TechnologyHangzhou310023China Electric Power Research Institute of State Grid Anhui Electric Power Co. Ltd.Hefei230601China State Grid Zhejiang Electric Power Co. Ltd.Wencheng County Power Supply CompanyWenzhou325000China Zhejiang Tusheng Power Transmission and Transformation Engineering Co. Ltd.Tusheng Technology BranchWenzhou325000China

An improved Fuzzy time series algorithmbased on clustering is designed in this paper.The algorithm is successfully applied to short-term load forecasting in the distribution stations.Firstly,the K-means clustering method is used to cluster the data,and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division.On this basis,the data is fuzzed to form a Fuzzy time series.Secondly,a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load,which is used to predict the short-term trend change of load in the distribution stations.Matlab/Simulink simulation results show that the load forecasting errors of the typical Fuzzy time series on the time scale of one day and one week are[−50,20]and[−50,30],while the load forecasting errors of the improved Fuzzy time series on the time scale of one day and one week are[−20,15]and[−20,25].It shows that the Fuzzy time series algorithm improved by clustering improves the prediction accuracy and can effectively predict the short-term load trend of distribution stations.

关键词： Short-term load forecasting Fuzzy time series K-means clustering distribution stations

A Fuzzy time series forecasting method induced by intuitionistic fuzzy sets

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International Journal of Modeling, Simulation, and Scientific Computing 2015年第4期6卷 130-152页

作者： Sanjay Kumar Sukhdev Singh Gangwar G.B.Pant University and Agriculture and Technology Pantnagar UttarakhandIndia

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.

关键词： Fuzzy time series intuitionistic fuzzy sets nondeterminacy induced fuzzy set data models.

A COMPUTATIONAL METHOD FOR Fuzzy time series FORECASTING BASED ON DIFFERENCE PARAMETERS

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International Journal of Modeling, Simulation, and Scientific Computing 2013年第1期4卷 22-33页

作者： BHAGAWATI P.JOSHI SANJAY KUMAR Department of Mathematics Statistics&Computer Science G.B.Pant University of Agriculture&Technology Pantnagar 263145UttarakhandIndia

Present study proposes a method for Fuzzy time series forecasting based on difference parameters.The developed method has been presented in a form of simple computational algorithm.It utilizes various difference parameters being implemented on current state for forecasting the next state values to accommodate the possible vagueness in the data in an efficient way.The developed model has been simulated on the historical student enrollments data of University of Alabama and the obtained forecasted values have been compared with the existing methods to show its superiority.Further,the developed model has also been implemented in forecasting the movement of market prices of share of State Bank of India(SBI)at Bombay Stock Exchange(BSE),India.

关键词： Fuzzy time series time variant fuzzy membership grade computational algorithm.

A Fuzzy time series Model Based on Improved fuzzy Function and Cluster Analysis Problem

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Communications in Mathematics and Statistics 2022年第1期10卷 51-66页

作者： Tai Vovan Thuy Lethithu College of Science Can Tho UniversityCan ThoVietnam Vinh Long University Technology and Education Vinh LongVietnam

Based on the improvement in establishing the relations of data,this study proposes a new Fuzzy time series model.In this model,the suitable number of fuzzy sets and their specific elements are determined automatically.In addition,using the percentage variations of series between consecutive periods of time,we build the fuzzy function.Incorporating all these improvements,we have a new Fuzzy time series model that is better than many existing ones through the well-known data sets.The calculation of the proposed model can be performed conveniently and efficiently by a MATLAB procedure.The proposed model is also used in forecasting for an urgent problem in Vietnam.This application also shows the advantages of the proposed model and illustrates its effectiveness in practical application.

关键词： Cluster analysis Forecast Fuzzy time series Model

A reduction algorithm of Fuzzy time series model based on a kind of new fuzzy sets

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A reduction algorithm of fuzzy time series model based on a ...

Handling higher order time series forecasting approach in intuitionistic fuzzy environment

第26届中国控制与决策会议

作者： CHEN Gang LI Jinling WANG Pengfei Department of Mathematics Dalian Maritime University

ISBN: (纸本)9781479937066

Facing the shortcomings of fuzzy set in Fuzzy time series, a new data fuzzification method is presented by a kind of new fuzzy sets based on distance sense. Then, the reduction algorithm of fuzzy rules is given by the characteristic expansion method. Finally, through the forecasting of Alabama university enrollments, the results show that the proposed method is effective.

关键词： Fuzzy time series fuzzy sets Characteristic coefficient Degree of correlation Rules reduction

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Journal of Control and Decision 2020年第4期7卷 327-344页

作者： Abhishekh Sanjay Kumar Department of Mathematics Institute of ScienceBanaras Hindu UniversityVaranasi 221005India

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].

关键词： Fuzzy time series intuitionistic fuzzy number membership grades accuracy function higher order IFLR

Forecasting Approach in Fuzzy time series Based on Information Granules

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Forecasting Approach in Fuzzy Time Series Based on Informati...