根據股票市場是非線性動力系統的假設，利用混沌理論對混沌時間序列的分析方法，提出了股票價格預測方法。同時利用重構相空間的嵌入維數和延遲時間分別確定經向基函數模型網絡的結構和訓練樣本對，對實際的股票時間序列預測結果表明，該方法能有效地進行短期預測，并與前饋神經網絡模型相比，可得到較好的預測結果，因而在股票時間序列預測中有廣泛的實用價值。
關 鍵 詞 混沌時間序列; 股票價格; 神經網絡; 預測

Abstract A method of stock price prediction is presented by hypothesis of stock market being non-linear dynamic system and analyzing method of chaos theory for chaos time series in this paper. Meanwhile, structures of radial basic function (RBF) network and pairs of training samples are determined by embedding dimension and delay time of reconstruct phase space respectively. Predicting results for real world stock time series show that the method is able to do effectively short-term prediction. In comparison with traditional forward feedback neural network (BP), the method can make better predicting performance, thus it can be widely used in stock price prediction.
Key words chaotic time series; stock price; neural network; prediction