Prediction of TAIEX based on hybrid fuzzy time series model with single optimization process

Yolcu Ö., Alpaslan F.

APPLIED SOFT COMPUTING, cilt.66, ss.18-33, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 66
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1016/j.asoc.2018.02.007
  • Dergi Adı: APPLIED SOFT COMPUTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.18-33
  • Anahtar Kelimeler: Fuzzy time series, Forecasting, Single multiplicative neuron model, Particle swarm optimization, MULTIPLICATIVE NEURON MODEL, AUTOMATIC CLUSTERING-TECHNIQUES, LOGICAL RELATIONSHIP GROUPS, FORECASTING ENROLLMENTS, TEMPERATURE PREDICTION, INFORMATION GRANULES, ANFIS MODEL, INTERVALS, ALGORITHM, NETWORKS
  • Marmara Üniversitesi Adresli: Hayır

Özet

All fuzzy time series approaches proposed in the literature consider three steps constituting the solution process as separate processes. Thus, model error is the sum of the errors that may occur in each step. In this regard, synchronous evaluation of the steps constituting the analysis process will produce a single model error and will lead to a reduction in the model error. Within the scope of this study, we proposed an approach which evaluates the steps constituting fuzzy time series analysis in one process synchronously to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index. In the proposed approach, defuzzification step is eliminated by using real values of time series as target values in the identification of fuzzy relations step. In this respect, determination of fuzzy cluster centres in fuzzification and the training of artificial neural network with single multiplicative neuron which is used the identification of fuzzy relation are carried out in a single optimization process with particle swarm optimization. This work also covers comprehensive literature review and summary info of related methodologies including fuzzy time series, particle swarm optimization, single multiplicative neuron model and fuzzy C-means clustering. The proposed method is applied to twelve different time series and a total of twenty-four measurements are done and superior forecasting performance of the method is proven. (C) 2018 Elsevier B.V. All rights reserved.