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首页> 外文期刊>Applied Soft Computing >Forecasting the monthly iron ore import of China using a model combining empirical mode decomposition, non-linear autoregressive neural network, and autoregressive integrated moving average
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Forecasting the monthly iron ore import of China using a model combining empirical mode decomposition, non-linear autoregressive neural network, and autoregressive integrated moving average

机译：使用模型组合实证分解，非线性自动评级神经网络和自动汇编综合移动平均水平的模型预测中国的每月铁矿石进口

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摘要

Considering that the non-linear path of monthly time-series for the iron ore imported to China is under reciprocal influences of multiple factors, the process of data generation is not easily represented in a time-series model. Based on the decomposition-integration method, superiorities of empirical mode decomposition (EMD), non-linear autoregressive neural network (NARNN), and autoregressive integrated moving average (ARIMA) models are integrated to establish a combined model EMD-NARNN-ARIMA. The empirical results show that, compared with the NARNN or seasonal autoregressive integrated moving average (SARIMA) models, the proposed model is more suitable for predicting data pertaining to the import of iron ore to China. The prediction error of EMD-NARNN-ARIMA is significantly lower than that of NAR and SARIMA, and, more importantly, it does not increase the time-complexity. The predicted result attained through the proposed model reveals that the import of iron ore to China from January 2019 to December 2020 will gradually decrease, accompanied by reasonable seasonal fluctuations, which is consistent with the decreasing trend in the demand for iron and steel as a result of the adjustment of China's current industrial structure. (C) 2020 Elsevier B.V. All rights reserved.
机译：考虑到为中国进口的铁矿石的月度时间系列的非线性路径受到多因素的互殖影响，数据生成过程不易在时间序列模型中表示。基于分解集成方法，集成了经验模式分解（EMD），非线性自回归神经网络（NARNN）和自回归集成移动平均（ARIMA）模型的优势，以建立组合模型EMD-NARNN-ARIMA。实证结果表明，与鼻环或季节性自回归综合移动平均线（Sarima）模型相比，所提出的模型更适合预测与中国铁矿石有关的数据。 EMD-NARNN-ARIMA的预测误差显着低于NAR和Sarima，更重要的是，它不会增加时间复杂性。通过拟议模型获得的预测结果表明，2019年1月至12月20日的铁矿石进口将逐步减少，伴随着合理的季节性波动，这与钢铁需求的趋势一致中国当前产业结构调整。 （c）2020 Elsevier B.V.保留所有权利。

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