A neural-network extension of the method of analogues for iterated time series prediction

被引：0

作者：

Hazarika, N ^{[1
]}

Lowe, D ^{[1
]}

机构：

[1] Aston Univ, Neural Comp Res Grp, Birmingham B4 7ET, W Midlands, England

来源：

NEURAL NETWORKS FOR SIGNAL PROCESSING VIII | 1998年

关键词：

D O I：

10.1109/NNSP.1998.710676

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this work we describe an algorithm for nonlinear iterated prediction of time series based on a neural network extension of the method of analogues proposed by Lorenz (J. Atm. Sci., 26, 636-646,1969). The present method is investigated in the context of iterated time series forecasting using embeddings of a nonlinear dynamical system. The approach yields significantly better results than published work on some of the Santa Fe competition data sets. In this paper the proposed technique is demonstrated by an application to a real world time series data of electricity load demand.

引用

页码：458 / 466

页数：9