基于小波和多维泰勒网动力学模型的金融时间序列预测

提出建立多维泰勒网动力学模型及参数辨识方法, 和基于小波多维泰勒网模型的金融时间序列预测方法. 利用Mallat算法将金融时间序列分解成一个低频信号和若干个高频信号; 对不同频率的时间序列建立多维泰勒网动力学模型; 通过共轭梯度法训练模型参数, 并进行预测; 将各模型的预测结果进行叠加, 得到原始序列的预测值. 实验结果表明, 这种金融时间序列预测方法具有较高的预测精度和预测方向正确率.

Financial time series forecasting based on wavelet and multi-dimensional Taylor network dynamics model

Presented in this paper is a new approach to establishment of the dynamics model of multi-dimensional Taylor network and its parameter identification, whereby a method based on the model and wavelet for financial time-series forecasting is proposed. The financial time series are decomposed into sub-series of a low frequency signal and several high frequency signals via Mallat algorithm, for each of which a multi-dimensional Taylor network model is established. Model parameters are trained by conjugate gradient method, and then the models are used for forecasting. All forecasting results are superimposed to obtain the predicted value of the original time series. As verified by our experiments, the proposed method works well in ensuring the accuracy of financial time-series forecasting.