An interpretable Neural Fuzzy Hammerstein-Wiener network for stock price prediction

• Main Authors: Xie, Chen, Rajan, Deepu, Chai, Quek
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Fuzzy Neural Network; Hammerstein-Wiener Model
• Online Access: https://hdl.handle.net/10356/159511
• Institution: Nanyang Technological University

An interpretable regression model is proposed in this paper for stock price prediction. Conventional offline neuro-fuzzy systems are only able to generate implications based on fuzzy rules induced during training, which requires the training data to be able to adequately represent all system behavio...