Inductive GMDH-Based Approach to Hierarchical Forecasting

被引:0
作者
Ivakhnenko, Gregory [1 ]
机构
[1] Bout Res Grp, London, England
来源
2018 IEEE 13TH INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE ON COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES (CSIT), VOL 1 | 2018年
关键词
Hierarchical modelling; Multilevel forecasting; GMDH; Harmonic models; Temporal aggregation;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In the paper, the inductive algorithms of hierarchical modelling for long-term forecasting are considered. GMDH algorithms are used to get accurate long-term forecasts on all levels of temporal data. Inductive approach allows to reconcile models and to increase accuracy of forecasting simultaneously on all levels of modelling. The results show that multi-level inductive algorithms can improve quality and extend forecast horizon in comparison with conventional univariate methods used in the hierarchical forecasting.
引用
收藏
页码:448 / 451
页数:4
相关论文
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