面向金融数据的神经网络时间序列预测模型

Neural network time series prediction model for financial data

针对Elman神经网络模型,通过引入时间权重与随机性因素,提出了改进的Elman神经网络模型,提高了现有Elman神经网络针对时序数据预测的精度。提出了基于时序数据的特征学习框架,可评估多个特征参数对结果的联合影响。在此基础上,提出了一个互联网金融风险预测模型,实验结果表明,所提出的模型在金融时序预测中具有更好的准确度。

This paper studied the Elman neural network model used in time series data predictions.It proposed an enhanced Elman model with the introduction of time weight and random factor for the improvement of prediction accuracy.In addition,it also proposed a feature selection framework as a part of the enhanced model for time series data training,the framework was able to evaluate the joint effect of multiple features.On this basis,it gave an Internet financial risk prediction model.The result indicates that the model has better accuracies in the predictions of financial time series data.