An Effective Stock Price Prediction Technique Using Hybrid Adaptive Neuro Fuzzy Inference System Based on Grid Partitioning

  • Atanu Chakraborty
  • Debojoyti Mukherjee
  • Amit Dutta
  • Aruna Chakraborty
  • Dipak Kumar Kole
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 385)


Prediction of forthcoming stock price is an important area of research. A large number of data is used by the system to predict the possible upcoming events in future. The stock prediction work is done primarily for overnight as it gets more volatile in a longer span. However in this work an effective effort is made to extend the duration of prediction to 14 days. A fuzzy logic approach based on grid partition is adopted in the paper to deal with the uncertainty factors while predicting the stock price of any company. The premise and consequent parameters of the learning rules are optimized in an adaptive fashion using a hybrid neural learning mechanism. This Adaptive Neuro-Fuzzy Inference System (ANFIS) using grid partition is undertaken to deal with the problem of stock price prediction, which leads to an accuracy of 94-95%.


Stock prediction Grid partitioning ANFIS Normalization 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Atanu Chakraborty
    • 1
  • Debojoyti Mukherjee
    • 1
  • Amit Dutta
    • 2
  • Aruna Chakraborty
    • 1
  • Dipak Kumar Kole
    • 3
  1. 1.Department of CSESt. Thomas’ College of Engineering & TechnologyKolkataIndia
  2. 2.Department of ITSt. Thomas’ College of Engineering & TechnologyKolkataIndia
  3. 3.Department of CSEJalpaiguri Government Engineering CollegeJalpaiguriIndia

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