The Research On Market Forecasting Based On BP Neural Network

There are many factors such as basic demand,seasonal factors,periodic factors and stochastic factors,which influence sales. Traditional prediction methods only consider some factors, and the forecasting model is too simple to describe the complicated relations between factors. Artificial neural network (ANN) has good capability nonlinear mapping and self-learning and it can solidify all the factors and the relations between factors into the sales forecasting model through learning. Therefore, based on Back Proragation (BP) algorithm, the model of sales forecasting is built. The predication results indicate that the sales forecast precision is improved, the capability of generalization is good and the model has practical value.The thesis studied the BP neural network's model and structure, its studying rule, built up the time sequence based on BP neural network. Also studied the problem like network size, generalization. The thesis brief introduced some basic knowledge on sales forecasting, neural network, and the basic principle of time sequence, all kinds of model, analyzed the time sequence forecasting method based on neural network. The thesis brief introduced how to realize BP neural network in MATLAB, introduced how to create, initialize, train and simulate a network. Also introduced some functions usually used in MATLAB. And wrote a program to realize the BP network.The thesis did a experiment on someone comply sales forecasting, proved that the model and research method is effective. It simply the network structure, enhanced the precision. The result came out to be not bad, demonstrate that the time sequence forecasting model based on BP network has a good ability to forecast and generalize. The thesis summarized the previous studying fruit, pointed at its disadvantages, brought author's opinions, put them into practice, hopping could help to do something on the research of market forecasting based on BP neural network.