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Time-series forecasting with a novel fuzzy time-series approach: An example for Istanbul stock market

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Time-series forecasting with a novel fuzzy time-series approach: An example for Istanbul stock market. / Yolcu, Ufuk; Aladag, Cagdas Hakan; Egrioglu, Erol et al.
In: Journal of Statistical Computation and Simulation, Vol. 83, No. 4, 01.04.2013, p. 597-610.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Yolcu, U, Aladag, CH, Egrioglu, E & Uslu, VR 2013, 'Time-series forecasting with a novel fuzzy time-series approach: An example for Istanbul stock market', Journal of Statistical Computation and Simulation, vol. 83, no. 4, pp. 597-610. https://doi.org/10.1080/00949655.2011.630000

APA

Yolcu, U., Aladag, C. H., Egrioglu, E., & Uslu, V. R. (2013). Time-series forecasting with a novel fuzzy time-series approach: An example for Istanbul stock market. Journal of Statistical Computation and Simulation, 83(4), 597-610. https://doi.org/10.1080/00949655.2011.630000

Vancouver

Yolcu U, Aladag CH, Egrioglu E, Uslu VR. Time-series forecasting with a novel fuzzy time-series approach: An example for Istanbul stock market. Journal of Statistical Computation and Simulation. 2013 Apr 1;83(4):597-610. doi: 10.1080/00949655.2011.630000

Author

Yolcu, Ufuk ; Aladag, Cagdas Hakan ; Egrioglu, Erol et al. / Time-series forecasting with a novel fuzzy time-series approach : An example for Istanbul stock market. In: Journal of Statistical Computation and Simulation. 2013 ; Vol. 83, No. 4. pp. 597-610.

Bibtex

@article{5e992dc4aba24c4faeae2b0e7aba097d,
title = "Time-series forecasting with a novel fuzzy time-series approach: An example for Istanbul stock market",
abstract = "Artificial intelligence procedures such as artificial neural networks (ANNs), genetic algorithms and particle swarm optimization and other procedures such as fuzzy clustering have been successfully used in the various stages of different fuzzy time-series forecasting approaches. Fuzzy clustering, genetic algorithm and particle swarm optimization are generally used in the fuzzification stage, and this simplifies the applicability of this stage and makes the fuzzy time-series approach more systematic. ANNs have also been applied successfully in the fuzzy relationship determination stage. In this study, we propose a new hybrid fuzzy time-series approach in which fuzzy c-means clustering procedure is employed in the fuzzification stage and feed-forward neural networks are used in the fuzzy relationship determination stage. This study also includes an empirical analysis pertaining to the forecasting of Index 100 for the stocks and bonds exchange market of Istanbul.",
keywords = "defuzzification, feed-forward neural networks, fuzzification, fuzzy c-means clustering, fuzzy relationship, fuzzy time series",
author = "Ufuk Yolcu and Aladag, {Cagdas Hakan} and Erol Egrioglu and Uslu, {Vedide R.}",
year = "2013",
month = apr,
day = "1",
doi = "10.1080/00949655.2011.630000",
language = "English",
volume = "83",
pages = "597--610",
journal = "Journal of Statistical Computation and Simulation",
issn = "0094-9655",
publisher = "Taylor and Francis Ltd.",
number = "4",

}

RIS

TY - JOUR

T1 - Time-series forecasting with a novel fuzzy time-series approach

T2 - An example for Istanbul stock market

AU - Yolcu, Ufuk

AU - Aladag, Cagdas Hakan

AU - Egrioglu, Erol

AU - Uslu, Vedide R.

PY - 2013/4/1

Y1 - 2013/4/1

N2 - Artificial intelligence procedures such as artificial neural networks (ANNs), genetic algorithms and particle swarm optimization and other procedures such as fuzzy clustering have been successfully used in the various stages of different fuzzy time-series forecasting approaches. Fuzzy clustering, genetic algorithm and particle swarm optimization are generally used in the fuzzification stage, and this simplifies the applicability of this stage and makes the fuzzy time-series approach more systematic. ANNs have also been applied successfully in the fuzzy relationship determination stage. In this study, we propose a new hybrid fuzzy time-series approach in which fuzzy c-means clustering procedure is employed in the fuzzification stage and feed-forward neural networks are used in the fuzzy relationship determination stage. This study also includes an empirical analysis pertaining to the forecasting of Index 100 for the stocks and bonds exchange market of Istanbul.

AB - Artificial intelligence procedures such as artificial neural networks (ANNs), genetic algorithms and particle swarm optimization and other procedures such as fuzzy clustering have been successfully used in the various stages of different fuzzy time-series forecasting approaches. Fuzzy clustering, genetic algorithm and particle swarm optimization are generally used in the fuzzification stage, and this simplifies the applicability of this stage and makes the fuzzy time-series approach more systematic. ANNs have also been applied successfully in the fuzzy relationship determination stage. In this study, we propose a new hybrid fuzzy time-series approach in which fuzzy c-means clustering procedure is employed in the fuzzification stage and feed-forward neural networks are used in the fuzzy relationship determination stage. This study also includes an empirical analysis pertaining to the forecasting of Index 100 for the stocks and bonds exchange market of Istanbul.

KW - defuzzification

KW - feed-forward neural networks

KW - fuzzification

KW - fuzzy c-means clustering

KW - fuzzy relationship

KW - fuzzy time series

U2 - 10.1080/00949655.2011.630000

DO - 10.1080/00949655.2011.630000

M3 - Journal article

AN - SCOPUS:84864746036

VL - 83

SP - 597

EP - 610

JO - Journal of Statistical Computation and Simulation

JF - Journal of Statistical Computation and Simulation

SN - 0094-9655

IS - 4

ER -