Time series identifying and modeling with neural networks

In this paper, a time series identifying and modeling method using neural networks is developed as an approximation tool for time series. Such a method can capture homeostatic dynamics of the system under the influence of exogenous event. The results show that financial time series include both predictable deterministic and unpredictable random components. Neural networks can identify the properties of homeostatic dynamics and model the dynamic relation between endogenous and exogenous variables in financial time series input-output system. In addition, we investigate the impact of the number of model inputs and the number of hidden layer neurons on time series analysis and financial forecasting.