Analysis Of Financial Time Series Based On Neural Network

With the development of economy and the conversion of people's investment consciousness, stock investment has become an important part of people's life in modern times. Stock market is a high-risk and high-interest domain, therefore through researches into its internal disciplinarian, effective analytic methods and tools are being looked for to gain more interest with lower risk. Therefore the study and prediction of disciplinarian in financial time series have great theoretical significant and applicable value.Financial time series has high randomicity and nonlinearity. Neural network is quite suitable in the process of financial time series data for its good ability of nonlinear mapping and generalization.In this paper, both neural network and ARIMA model are used in analyzing the forecast of SP&500 and Fuhua Ltd. stock rate. Also comparisons of the two results are made.In the forecast process, because there is no systemic method for calculating the structure and parameter, lots of factors can influence the neural network. Every forecast method has some special adaptive scope and weaknesses because of its own characteristic. The paper uses the combined method to forecast in order to overcome the weaknesses of one single method. Research result shows that this method can effectively overcome the instability of single Neural NetWork.Both theoretical analysis and experimental results show that neural network has special advantage in financial analysis and forecasting. It will be widely used in the future.