Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis

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A new accuracy measure based on bounded relative error for time series forecasting (2017, background): “Esfahanipour and Aghamiri [17] proposed a model with an error of 1.3%, which appears to be good.”
Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration (2018, background): “More information about the FCM can be found in (Abdulshahed et al. 2015; Esfahanipour and Aghamiri 2010).”
Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting (2014, methodology): “According to an extensive literature investigation, it is not difficult to find a wide variety of methodologies and techniques that have been used for stock price forecasting with various degrees of success, such as the Box–Jenkins method [1], general autoregressive conditional heteroskedasticity [2”
Financial forecasting using ANFIS networks with Quantum-behaved Particle Swarm Optimization (2014, methodology): “Esfahanipour and Aghamiri (2010) used technical indices as the input variables for ANFIS on a Takagi Sugeno Kang (TSK) type fuzzy system and fuzzy C-mean clustering for stock price prediction.”
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting (2010, background): “Among them, in recent years some researcher have used SRA for input variable selection in the field of stock market forecasting and they have obtained very promising results [7,17].”