Stock price prediction using Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) fuzzy neural network

This paper analyses stock market price prediction based on a Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) fuzzy neural network. Stock price prediction is a problem that requires online adaptive systems with high accuracy performance. The proposed GSETSK framework uses a novel Multidimensional-Scaling Growing Clustering (MSGC) algorithm which mimics the human cognitive process to flexibly generate fuzzy rules without any a prior knowledge. MSGC can quickly generate a compact fuzzy rule base from new incoming data and has strong noise-tolerance capability. It empowers the GSETSK network with the ability to effectively address adaptive and incremental problems such as stock price prediction. Numerical experiments conducted on real-life stock data confirm the validity of the design and the accuracy performance of the GSETSK system.