Input window size and neural network predictors
Input window size and neural network predictors
复制标题
输入窗口大小和神经网络预测器
DOI：
10.1109/ijcnn.2000.857903
复制
发表时间：
2000
期刊：
影响因子：
--
通讯作者：
S. Hunt
中科院分区：
文献类型：
--
作者：
R. Frank;N. Davey;S. Hunt
简要讨论了时间序列预测的神经网络方法，并确定了指定适当大小的输入窗口的必要性。简要介绍了动态系统理论的相关理论结果，并讨论了如何找到正确的嵌入维数和窗口大小的算法。该方法被应用到两个时间序列和所产生的泛化性能的训练前馈神经网络预测进行了分析。它表明，在定义适当的网络体系结构的物理学可以提供有用的信息。
Neural network approaches to time series prediction are briefly discussed, and the need to specify an appropriately sized input window identified. Relevant theoretical results from dynamic systems theory are briefly introduced, and heuristics for finding the correct embedding dimension, and hence window size, are discussed. The method is applied to two time series and the resulting generalisation performance of the trained feedforward neural network predictors is analysed. It is shown that the heuristics can provide useful information in defining the appropriate network architecture.