Pattern Extraction from Financial Time Series Based on Neural Networks

In this paper, a relatively new pattern extraction technique based on neural networks is developed as an approximation tool for financial time series. Such technique can capture homeostatic dynamics of the system under the influence of exogenous event. 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. We also investigate the impact of the number of model inputs and the number of hidden layer neurons on financial analysis.