D-GMDH: A novel inductive modelling approach in the forecasting of the industrial economy

D-GMDH: A novel inductive modelling approach in the forecasting of the industrial economy
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D-GMDH:工业经济预测中的一种新颖的归纳建模方法

DOI:
10.1016/j.econmod.2012.09.021
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发表时间:
2013
期刊:
影响因子:
4.7
通讯作者:
Bing Zhu
Bing Zhu
中科院分区:
经济学2区
文献类型:
--
作者:
Mingzhu Zhang;Changzheng He;Xin Gu;Panos Liatsis;Bing Zhu

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本文提出了一种新的预测模型,用于分析中国四川省的经济发展。该模型引入了多样性的概念,是基于改进的-GMDH算法。将该方法与Dutta(2009)提出的两种集成方法进行了比较,结果表明D-GMDH方法的预测精度优于这两种方法。并将D-GMDH模型应用于四川省工业增加值的预测。将所得结果与传统的GMDH模型、GMDH组合模型和广泛应用的阿尔马模型进行了比较。结果表明,D-GMDH具有较好的预测精度,是数据受噪声污染时进行经济预测的有效手段。
This work proposes a new forecasting model to analyse the economic development of Sichuan province of China. The model, which introduces the concept of diversity, is based on an improvement of the -GMDH algorithm. The new method, called D-GMDH, is compared with two ensemble approaches which are introduced by Dutta (2009), and D-GMDH is better than the two approaches in forecasting accuracy. D-GMDH is also applied to forecast the industrial added value of the Sichuan province. The obtained results are compared with those of the traditional GMDH model, GMDH combination model and the widely used ARMA model. The results show that D-GMDH has good prediction accuracy and is an effective means for economic forecasting when data is contaminated by noise.
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