A Deep Hybrid Fuzzy Neural Hammerstein-Wiener Network for Stock Price Prediction

A deep hybrid fuzzy neural Hammerstein-Wiener model (FNHW), is proposed in this paper. The implication and inference of a neuro-fuzzy is based on the fuzzy rulebase that has been formed during traning. It requires the training data to be able to adequately represent entire system behaviors. However, the test data may vary with distribution shift in time series domain. Furthers, the training data may be derived from steady-state while the test data which is in the form of dynamically changing represented by drastic data shift under certain scenario such as financial crisis. A hybrid approach is proposed to employ neuro-fuzzy system to make prediction on steady-state data in parallel with the Hammerstein-Wiener model to predict the dynamically changing behavior. This is implemented by MLP as the control unit to decide the scale of contribution that each system is made to the final prediction. By doing this, the soundness of rulebase inference from neuro-fuzzy system on the steady-state data is achieved as well as inheriting the good approximation accuracy and excellent asymptotic tracking advantages of Hamerstein-Wiener model on the dynamically changing data. The effectiveness of proposed model is evaluated on two financial stock price prediction dataset. Experimental results showed that FNHW outperforms other neuro-fuzzy methods for both dataset.