A new approach for determining the length of intervals for fuzzy time series

Yolcu, Ufuk; Eğrioğlu, Erol; Uslu, Vedide Rezan; Başaran, Murat Alper; Aladağ, Çağdaş Hakan

doi:10.1016/j.asoc.2008.09.002

Cited by 121 publications

(48 citation statements)

References 17 publications

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“…Moreover, Huarng and Yu [59] improved the method by utilizing the ratio-based model from the viewpoint of opinion that inconstant interval length can improve the forecasting performance. Optimization of ratio has been put forward by Yolcu et al [97]. Recently, some heuristic optimization algorithms have been used to get better results by Kuo et al [65,66], Davari, et al [43], Park et al [77] and Hsu et al [56], Chen and Chung [29], Lee et al [69,70].…”

Section: Literature Reviewmentioning

confidence: 99%

A combined robust fuzzy time series method for prediction of time series

Yolcu

Lam

2017

Neurocomputing

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In case of outlier(s) it is inevitable that the performance of the fuzzy time series prediction methods is influenced adversely. Therefore, current prediction methods will not be able to provide satisfactory accuracy rates for defuzzified outputs (predictions) when the data has outlier(s). In this study, not only to be able to sort out this problem but also to be able to improve the forecasting accuracy, we propose a combined robust approach for fuzzy time series by assessing how the prediction performance of the methods will be affected from the

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Section: Literature Reviewmentioning

confidence: 99%

A combined robust fuzzy time series method for prediction of time series

Yolcu

Lam

2017

Neurocomputing

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“…Yolcu et al [18] proposed a new approach using a single-variable constrained optimisation to determine the ratio for the length of intervals. To find the correlation between stock volume and stock market price, Chu et al [19] proposed a dual-factor method.…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…To determine the discourse division based on the heuristic, considering the effects of divided intervals on prediction results [8]. In addition, there are also discourse division methods based on natural division [9], distribution density [10,13,17], interval length of proportion [11], genetic algorithm [14], and optimization algorithm of single variable constraint [16].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Time Series Prediction Method Based on Spectral Clustering

Zhou¹,

Huang²,

Li³

et al. 2017

dtetr

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Abstract. The accuracy of fuzzy time series prediction method depends on the accuracy of the discourse division and fuzzy relation extraction. Aiming at the main problems in the model, this paper put forward a kind of fuzzy time series prediction method based on spectral clustering using spectral clustering dividing time series discourse and calculate the corresponding fuzzy sets to construct fuzzy time series; At the same time using second-order Markov probability model constructing fuzzy relation in fuzzy time series, and then predict the subsequent numerical. Compared with the traditional fuzzy time series prediction method, this method can improve the accuracy.

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A new approach for determining the length of intervals for fuzzy time series

Cited by 121 publications

References 17 publications

A combined robust fuzzy time series method for prediction of time series

A combined robust fuzzy time series method for prediction of time series

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

A Fuzzy Time Series Prediction Method Based on Spectral Clustering

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