An Enhanced Deterministic Fuzzy Time Series Forecasting Model

Li, Sheng-Tun; Cheng, Yi-Chung

doi:10.1080/01969720802715128

Cited by 16 publications

(9 citation statements)

References 22 publications

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“…The experimental results and analysis confirm the superiority of the proposed model in forecasting accuracy and reliability. Future works will aim at generalizing the proposed model to handle multi-factor forecasting problems; however the ratio of rule matching for forecasting becomes lower when more factors are taken into consideration, bringing out the issue of rule redundancy addressed in [35]. Other interesting future works involve applying the proposed model to deal with more complicated real-world problems.…”

Section: Discussionmentioning

confidence: 92%

A FCM-based deterministic forecasting model for fuzzy time series

Cheng

Lin

2008

Computers & Mathematics with Applications

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114

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a b s t r a c tThe study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling uncertainty and vagueness inherent in the data collected. A variety of forecasting models including high-order models have been devoted to improving forecasting accuracy. However, the high-order forecasting approach is accompanied by the crucial problem of determining an appropriate order number. Consequently, such a deficiency was recently solved by Li and Cheng [S.-T. Li, Y.-C. Cheng, Deterministic Fuzzy time series model for forecasting enrollments, Computers and Mathematics with Applications 53 (2007) 1904-1920] using a deterministic forecasting method. In this paper, we propose a novel forecasting model to enhance forecasting functionality and allow processing of two-factor forecasting problems. In addition, this model applies fuzzy cmeans (FCM) clustering to deal with interval partitioning, which takes the nature of data points into account and produces unequal-sized intervals. Furthermore, in order to cope with the randomness of initially assigned membership degrees of FCM clustering, Monte Carlo simulations are used to justify the reliability of the proposed model. The superior accuracy of the proposed model is demonstrated by experiments comparing it to other existing models using real-world empirical data.

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Section: Discussionmentioning

confidence: 92%

A FCM-based deterministic forecasting model for fuzzy time series

Cheng

Lin

2008

Computers & Mathematics with Applications

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114

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“…For example, we extended Li and Cheng's research framework to include a two-factor high-order timeinvariant fuzzy time series model [11]. Li and Cheng [12] also proposed a random hidden Markov model for twofactor one-order time-invariant fuzzy time series problems.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Fuzzy Relational Modeling for Fuzzy Time Series Forecasting

Kuo

Chen

2015

Int. J. Fuzzy Syst.

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The use of fuzzy time series has attracted considerable attention in studies that aim to make forecasts using uncertain information. However, most of the related studies do not use a learning mechanism to extract valuable information from historical data. In this study, we propose an evolutionary fuzzy forecasting model, in which a learning technique for a fuzzy relation matrix is designed to fit the historical data. Taking into consideration the causal relationships among the linguistic terms that are missing in many existing fuzzy time series forecasting models, this method can naturally smooth the defuzzification process, thus obtaining better results than many other fuzzy time series forecasting models, which tend to produce stepwise outcomes. The experimental results with two real datasets and four indicators show that the proposed model achieves a significant improvement in forecasting accuracy compared to earlier models.

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“…Lee et al [20,33] also presented some highorder models based on two-factor and genetic-simulated annealing techniques. Most of time series researchers [18,22,[34][35][36][37] had showed their, respectively, interest in high-order fuzzy time series forecasting models.…”

Section: Introductionmentioning

confidence: 99%

“…The second one is the class represented by Singh [16][17][18] whose models are on the basis of computational method with difference parameters. The last but not least one is the kind of models based on grouping the FLRs represented by [9,28,29,31,33,36,37]. In general, the first kind of hybrid models can get higher forecasting accuracy than the other two.…”

Section: Introductionmentioning

confidence: 99%

High-Order Fuzzy Time Series Model Based on Generalized Fuzzy Logical Relationship

Qiu

Liu

2013

Mathematical Problems in Engineering

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In view of techniques for constructing high-order fuzzy time series models, there are three methods which are based on advanced algorithms, computational methods, and grouping the fuzzy logical relationships, respectively. The last kind model has been widely applied and researched for the reason that it is easy to be understood by the decision makers. To improve the fuzzy time series forecasting model, this paper presents a novel high-order fuzzy time series models denoted asGTS(M,N)on the basis of generalized fuzzy logical relationships. Firstly, the paper introduces some concepts of the generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the proposed model is implemented in forecasting enrollments of the University of Alabama. As an example of in-depth research, the proposed approach is also applied to forecast the close price of Shanghai Stock Exchange Composite Index. Finally, the effects of the number of orders and hierarchies of fuzzy logical relationships on the forecasting results are discussed.

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An Enhanced Deterministic Fuzzy Time Series Forecasting Model

Cited by 16 publications

References 22 publications

A FCM-based deterministic forecasting model for fuzzy time series

A FCM-based deterministic forecasting model for fuzzy time series

Evolutionary Fuzzy Relational Modeling for Fuzzy Time Series Forecasting

High-Order Fuzzy Time Series Model Based on Generalized Fuzzy Logical Relationship

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