关键词: 股票价格, 组合预测, 神经网络, 自回归移动差分模型

Abstract: The stock price is mutant, nonlinear and random. Single prediction methods can only describe the stock price segment information defect. This paper proposes a stock price combination forecasting model. Autoregressive moving average is used to forecast the stock price’s linear trend, and then the RBF neural network is used to capture the nonlinear part. The two results are combined to form the stock price forecasting results. The simulation experiment is carried out on Baotou Steel shares（600010）. The results show that, compared with single forecast model, the proposed combination forecasting model can describe stock price change rules more comprehensively and accurately. It improves the forecasting precision of stock price.

Key words: stock price, combination forecasting, neural network, autoregressive integrating moving average