Exchange Rate Forecasting Method Based on Particle Swarm Optimization and Probabilistic Neural Network Model

Abstract

Foreign exchange market is a complex market, with a high degree of volatility characteristics. Exchange rate formation mechanism and the factors affecting exchange rate volatility is also very complex, is a nonlinear system, it is difficult to accurately forecast, probabilistic neural network is applied to the frontiers of forecast, and aimed at the characteristics of probabilistic neural network to pretreatment the exchange of data and forecast the tendency. And by changing the vector dimensionality experiment obtain the best entry to embed dimensionality, based on the model, particle swarm optimization algorithm applied in the probabilistic neural network to optimize the smoothing factors, tested and improved the precise prediction and valuable.