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

Yolcu U., ALADAĞ Ç. H., Egrioglu E., Uslu V. R.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, cilt.83, sa.4, ss.597-610, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 83 Sayı: 4
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1080/00949655.2011.630000
  • Dergi Adı: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.597-610
  • Anahtar Kelimeler: defuzzification, fuzzy c-means clustering, fuzzy time series, fuzzification, fuzzy relationship, feed-forward neural networks, ARTIFICIAL NEURAL-NETWORKS, GENETIC ALGORITHMS, ENROLLMENTS, INTERVALS, LENGTH, MODEL, OPTIMIZATION
  • Marmara Üniversitesi Adresli: Hayır

Özet

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.