基于ARIMA-BP神经网络组合模型的无锡不锈钢市场价格预测与分析

Yinshan Guo, Chunhui Zhao, Xiaoxiao Qin, Jin Zhang
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引用次数: 1

摘要

:不锈钢不仅是一种高强度、高韧性的结构材料,而且是一种经济的功能材料。我国不锈钢工业在建筑、交通、能源、包装等领域占有举足轻重的地位。研究其价格趋势和内部规律也尤为重要。因此,本文选取无锡市201系列热轧冷轧不锈钢和304系列热轧冷轧不锈钢的价格作为研究对象,分别采用时间序列模型和BP神经网络模型对其进行建模,预测未来不锈钢的价格。然后建立ARIMA-BP并联组合模型,并通过优势矩阵法对两个模型的权重进行分配,得到新的预测数据。通过对绝对误差和相对误差的比较分析,得出联合预测模型的精度高于时间序列和BP神经网络等基本预测模型。此外,本文还对不锈钢价格波动的影响因素进行了分析,并通过Pearson系数检验了不锈钢价格与各因素之间的显著相关程度,并对可能的原因进行了解释。最后,对不锈钢的未来发展进行了展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction and Analysis of Wuxi Stainless Steel Market Price Based on ARIMA-BP Neural Network Combination Model
: Stainless steel is not only a high-strength and high-toughness structural material, but also an economical functional material. My country’s stainless steel industry occupies a pivotal position in the fields of construction, transportation, energy, and packaging. It is also particularly important to study its price trends and internal laws. Therefore, this paper selects the price of 201 series hot-rolled and cold-rolled stainless steel and 304-series hot-rolled and cold-rolled stainless steel in Wuxi City as the research objects, and uses the time series model and BP neural network model to model them respectively, and predicts the prices of future stainless steel .Then a parallel ARIMA-BP combined model is established, and the weights of the two models are distributed by the advantage matrix method to obtain new forecast data. Comparing and analyzing absolute error and relative error, it is concluded that the accuracy of combined prediction model is higher than that of basic prediction models such as time series and BP neural network. In addition, this paper analyzes the factors influencing the fluctuations in the price of stainless steel, and the significant degree of correlation between stainless steel price and various factors is tested by the Pearson coefficient, and the possible reasons for it are explained. Finally, the future development of stainless steel is prospected.
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