基于神经树的时间序列预测

Time-Series Forecasting Based on Neural Tree Model

提出了一种新的神经树模型来进行时间序列预测。采用语法引导的遗传编程来进化神经树的结构以建立一个时间序列预测模型,并把它和基于神经网络的时间序列预测模型的性能进行比较,结果显示本文提出的神经树时间序列预测模型较神经网络模型有更高的可信度。

A new neural tree for modeling the timerseries forecasting is proposed in the paper. Grammar guided genetic programming was used to optimize the structure of neural tree for modeling the time-series forecasting in this paper, and we compared the performance with an artificial network model for time- series forecasting. The result shows that our model has a higher reliability than the artificial network model.