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

Yolcu, Ufuk and Aladag, Cagdas Hakan and Egrioglu, Erol and Uslu, Vedide 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). pp. 597-610. ISSN 0094-9655

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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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Statistical Computation and Simulation
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2604
Subjects:
ID Code:
139546
Deposited By:
Deposited On:
17 Dec 2019 09:20
Refereed?:
Yes
Published?:
Published
Last Modified:
26 Aug 2020 04:50