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

机译:使用新颖的模糊时间序列方法进行时间序列预测:伊斯坦布尔股票市场的示例

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摘要

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.
机译:诸如人工神经网络(ANN),遗传算法和粒子群优化之类的人工智能程序以及诸如模糊聚类之类的其他程序已成功用于不同的模糊时间序列预测方法的各个阶段。模糊化阶段一般采用模糊聚类,遗传算法和粒子群算法,简化了该阶段的适用性,使模糊时间序列方法更加系统化。人工神经网络已经成功地应用于模糊关系确定阶段。在这项研究中,我们提出了一种新的混合模糊时间序列方法,其中在模糊化阶段采用模糊c均值聚类过程,在模糊关系确定阶段采用前馈神经网络。这项研究还包括与伊斯坦布尔股票和债券交易市场的指数100预测有关的经验分析。

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