Time series forecasting: An approach to modeling seasonality using neural networks

被引：0
|
作者：
Dhawan, R [1 ]
O'Connor, M [1 ]
机构：
[1] Univ Sydney, Sch Business, Sydney, NSW 2006, Australia
来源：
ICAI '05: PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2 | 2005年
关键词：
time series; forecasting; neural networks; seasonality;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Artificial neural networks have been widely advocated as an alternative to statistical methods to forecast time series. Whether or not neural networks are able to model the seasonal component of a time series has been a matter of controversy. While some earlier studies suggest that neural networks can learn the seasonal component well, a couple of recent studies suggest that it is better to deseasonalise the data first. In this paper we present a neural network approach that reduces the forecasting errors of a seasonal series even without prior deseasonalisation of the data.
引用
收藏
页码：41 / 45
页数：5
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