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The application of neural networks to forecast fuzzy time series

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  • Huarng, Kunhuang
  • Yu, Tiffany Hui-Kuang

Abstract

Fuzzy time series models have been applied to handle nonlinear problems. To forecast fuzzy time series, this study applies a backpropagation neural network because of its nonlinear structures. We propose two models: a basic model using a neural network approach to forecast all of the observations, and a hybrid model consisting of a neural network approach to forecast the known patterns as well as a simple method to forecast the unknown patterns. The stock index in Taiwan for the years 1991–2003 is chosen as the forecasting target. The empirical results show that the hybrid model outperforms both the basic and a conventional fuzzy time series models.

Suggested Citation

  • Huarng, Kunhuang & Yu, Tiffany Hui-Kuang, 2006. "The application of neural networks to forecast fuzzy time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(2), pages 481-491.
    • Handle: RePEc:eee:phsmap:v:363:y:2006:i:2:p:481-491
    DOI: 10.1016/j.physa.2005.08.014
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    References listed on IDEAS

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    1. Huarng, Kunhuang & Yu, Hui-Kuang, 2005. "A Type 2 fuzzy time series model for stock index forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 445-462.
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    1. Kaur, Gurbinder & Dhar, Joydip & Guha, Rangan Kumar, 2016. "Minimal variability OWA operator combining ANFIS and fuzzy c-means for forecasting BSE index," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 122(C), pages 69-80.
    2. Dombi, József & Jónás, Tamás & Tóth, Zsuzsanna Eszter, 2018. "Modeling and long-term forecasting demand in spare parts logistics businesses," International Journal of Production Economics, Elsevier, vol. 201(C), pages 1-17.
    3. Pal, Shanoli Samui & Kar, Samarjit, 2019. "Time series forecasting for stock market prediction through data discretization by fuzzistics and rule generation by rough set theory," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 162(C), pages 18-30.
    4. Chen, Tai-Liang & Cheng, Ching-Hsue & Jong Teoh, Hia, 2007. "Fuzzy time-series based on Fibonacci sequence for stock price forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 377-390.
    5. Kun-Huang Huarng & Tiffany Hui-Kuang Yu & Francesc Solé Parellada, 2010. "An innovative regime switching model to forecast Taiwan tourism demand," The Service Industries Journal, Taylor & Francis Journals, vol. 31(10), pages 1603-1612, March.
    6. Aladag, Cagdas Hakan & Yolcu, Ufuk & Egrioglu, Erol, 2010. "A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(4), pages 875-882.
    7. Chen, Tai-Liang & Cheng, Ching-Hsue & Teoh, Hia-Jong, 2008. "High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 876-888.
    8. Vedide Rezan USLU & Eren BAS & Ufuk YOLCU & Erol EGRIOGLU, 2013. "A New Fuzzy Time Series Analysis Approach By Using Differential Evolution Algorithm And Chronologically-Determined Weights," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 2(1), pages 18-30, JULY.
    9. Tai-Liang Chen, 2012. "Forecasting the Taiwan Stock Market with a Novel Momentum-based Fuzzy Time-series," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 38-50, February.
    10. Cheng, Ching-Hsue & Wei, Liang-Ying, 2014. "A novel time-series model based on empirical mode decomposition for forecasting TAIEX," Economic Modelling, Elsevier, vol. 36(C), pages 136-141.
    11. Lahmiri, Salim, 2016. "Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 388-396.
    12. Dong, Ruijun & Pedrycz, Witold, 2008. "A granular time series approach to long-term forecasting and trend forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3253-3270.
    13. Wei, Liang-Ying, 2013. "A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX," Economic Modelling, Elsevier, vol. 33(C), pages 893-899.
    14. Tai Vovan, 2019. "An improved fuzzy time series forecasting model using variations of data," Fuzzy Optimization and Decision Making, Springer, vol. 18(2), pages 151-173, June.
    15. Cheng, Ching-Hsue & Wei, Liang-Ying & Liu, Jing-Wei & Chen, Tai-Liang, 2013. "OWA-based ANFIS model for TAIEX forecasting," Economic Modelling, Elsevier, vol. 30(C), pages 442-448.

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