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

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<mark>Journal publication date</mark>1/04/2013
<mark>Journal</mark>Journal of Statistical Computation and Simulation
Issue number4
Number of pages14
Pages (from-to)597-610
Publication StatusPublished
<mark>Original language</mark>English


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.