Neural networks for event extraction from time series: a back propagation algorithm approach

This paper presents a relatively new event detection method using neural networks for time series analysis. Such 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. We explore the signaling mechanisms that transfer information in such dynamic system and investigate the impact of the number of model inputs and the number of hidden layer neurons on financial analysis.