Forecasting Fuzzy Time Series on a Heuristic High-Order Model

Own, Chung-Ming; Yu, Pao-Ta

doi:10.1080/01969720591008922

Cited by 56 publications

(21 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Later, Own and Yu [11] extended Chen's model [9] as a heuristic high-order fuzzy time series model, which depends strongly on the trend of fuzzy time series. Huarng and Yu [12,13] applied neural networks and type 2 fuzzy set to refine the fuzzy relationships and further build up forecasting accuracy.…”

Section: Introductionmentioning

confidence: 98%

“…In spite of the aforementioned approaches, the most commonly used method for interval partitioning, perhaps, is equalwidth partition [1,3,[5][6][7]9,11,16,23], however when the distribution of the continuous values is not uniform, it might not yield good results [24]. In addition, it has been recently shown that using unequal-sized intervals might produce better forecasting accuracy than traditional equal-width partitioning [22].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A FCM-based deterministic forecasting model for fuzzy time series

Cheng

Lin

2008

Computers & Mathematics with Applications

115

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

A FCM-based deterministic forecasting model for fuzzy time series

Cheng

Lin

2008

Computers & Mathematics with Applications

115

View full text Add to dashboard Cite

show abstract

“…Meanwhile, Chen [7] generalized their previous work [4] to deal with high-order time series, and high-order models have subsequently received more attention in the literature. Own and Yu [8] combined the concepts of fuzzy logic relationship groups and domainspecific heuristic knowledge, and proposed a forecasting model using a trend heuristic for fuzzy time series, while Lee et al [9] similarly extended Chen's model [7] to allow two-factor high-order time-invariant forecasting. Although high-order models have been shown to improve forecasting accuracy, there is a significant problem with regard to determining an appropriate order number and order redundancy.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Fuzzy Relational Modeling for Fuzzy Time Series Forecasting

Kuo

Chen

2015

Int. J. Fuzzy Syst.

View full text Add to dashboard Cite

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.

show abstract

“…In time-invariant fuzzy time series, several researchers have proposed various forecasting methods (Song and Chissom 1993b;Chen 1996;Huarng 2001a;Chen 2002;Lee and Chou 2004;Huarng and Yu 2005;Own and Yu 2005;Tsaur et al 2005;Yu 2005a,b;Chen and Chung 2006;Lee et al 2006;Liu 2007). Their forecasting methods often form fuzzy logical relationships (such as A i → A m , A i , .…”

mentioning

confidence: 99%

Improved time-variant fuzzy time series forecast

Liu

Wei

Yang

2009

Fuzzy Optim Decis Making

View full text Add to dashboard Cite

Since Song and Chissom (Fuzzy Set Syst 54:1-9, 1993a) first proposed the structure of fuzzy time series forecast, researchers have devoted themselves to related studies. Among these studies, Hwang et al. (Fuzzy Set Syst 100:217-228, 1998) revised Song and Chissom's method, and generated better forecasted results. In their method, however, several factors that affect the accuracy of forecast are not taken into consideration, such as levels of window base, length of interval, degrees of membership values, and the existence of outliers. Focusing on these factors, this study proposes an improved fuzzy time series forecasting method. The improved method can provide decision-makers with more precise forecasted values. Two numerical examples are employed to illustrate the proposed method, as well as to compare the forecasting accuracy of the proposed method with that of two fuzzy forecasting methods. The results of the comparison indicate that the proposed method produces more accurate forecasting results.

show abstract

Forecasting Fuzzy Time Series on a Heuristic High-Order Model

Cited by 56 publications

References 9 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

Improved time-variant fuzzy time series forecast

Contact Info

Product

Resources

About