Fuzzy relation analysis in fuzzy time series model

Tsaur, Ruey-Chyn; Yang, Jia-Chi O; Wang, Hsiao‐Fan

doi:10.1016/j.camwa.2004.07.014

Cited by 109 publications

(47 citation statements)

References 10 publications

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“…The prediction accuracy of simulating the prediction of historical data of time series reaches the most ideal level. The forecasting model of this paper has obvious advantages over the existing fuzzy time series forecasting models (such as [5][6][7][8][9][10][11][12][13][14][15][16]). …”

Section: Introductionmentioning

confidence: 99%

A Set of Ternary Time Series Forecasting Models Based on the Difference Rate

Lü¹,

Wang²,

Yin³

et al. 2018

dtcse

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Abstract.A set of ternary time series forecasting models based on the difference rate (ASTDR) is proposed. For an arbitrary time series, we can apply automatic optimization search method to sieve the ordinary time series forecasting model in ASTDR. For example, when simulating the prediction of the enrollments of University of Alabama in 1971-1992, we can apply automatic optimization search method to sieve the ordinary time series forecasting model Ft(0.000003,0.7, 0.000003) in ASTDR. The mean square error (MSE) and the average forecasting error rate (AFER) of the predicted values of the enrollments can reach MSE=0 and AFER=0%. The prediction accuracy of simulating the prediction of historical data of time series reaches the most ideal level.

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Section: Introductionmentioning

confidence: 99%

A Set of Ternary Time Series Forecasting Models Based on the Difference Rate

Lü¹,

Wang²,

Yin³

et al. 2018

dtcse

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“…To overcome this drawback, this study uses the frequency weighted method [2]. In this case the fuzzy logic relationship (FLR) that occurred often in the past will likely happen often in the future.…”

Section: ] Suppose F(t) Is Caused By F(t-1) Only and F(t) = F(t-1)×rmentioning

confidence: 99%

“…However, they cannot deal with time series vagueness and linguistic terms [1]. In addition, these statistical methods could not perform appropriately on time series with a small amount of data [2]. To deal with such deficiencies, fuzzy time series have been developed and widely applied.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Method Based on High Order Fuzzy Time Series and Simulated Annealing Technique

Radmehr

Gharneh

2011

The South African Journal of Industrial Engineering

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This paper proposes a fuzzy forecasting problem to forecast the Alabama University enrolment dataset. A novel simulated annealing heuristic algorithm is used to promote the accuracy of forecasting. The algorithm enjoys two new neighbourhood search operators called 'subtitle' and 'adjust'. A Taguchi method is also used as an optimisation technique to tune the different parameters and operators of the proposed model comprehensively. The experimental results show that the proposed model is more accurate than existing models. OPSOMMINGDie navorsing handel oor 'n voorgestelde wasige vooruitskatting vir studente-inskrywing by die Universiteit van Alabama. 'n Nuwerwetse louter heuristiese simulasie-algoritme word gebruik. Die model word onder andere beheer deur 'n Taguchi optimiseringstegniek. Die modelresultate toon 'n verbetering op ander bestaande metodes. a a a a a a a a a a a aa a a a a a a a a a a a a

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“…This is a typical time series without any rules. Up to now, although many scholars have proposed the fuzzy time series forecasting models (for example, the forecasting models proposed in [7][8][9][10][11][12][13][14]) to simulate the prediction of the enrollment of the University of Alabama in 1971-1992, the AFER and MSE of the predicted value of the enrollment are still large.…”

Section: Introductionmentioning

confidence: 99%