发帖
雷达卡
摘要翻译：
钢铁工业对发达国家和不发达国家的经济和环境都有重大影响。这一行业的重要性和这些影响促使许多研究者研究一个国家的钢铁消费与其经济活动之间的关系，从而产生了所谓的使用强度模型。本文以伊朗钢铁消费为例，考察了使用强度模型的有效性，并利用经济活动指数对该假设进行了扩展。我们使用所提出的模型训练支持向量机，并预测伊朗钢铁消费量的未来值。本文对模型中所用因素进行了详细的相关检验，以检验它们与钢材消费量之间的关系。结果表明，伊朗的钢铁消费与其经济活动密切相关，与过去40年的经济模式相同。
---
英文标题：
《Modeling and Prediction of Iran's Steel Consumption Based on Economic
Activity Using Support Vector Machines》
---
作者：
Hossein Kamalzadeh, Saeid Nassim Sobhan, Azam Boskabadi, Mohsen
Hatami, Amin Gharehyakheh
---
最新提交年份：
2019
---
分类信息：
一级分类：Economics 经济学
二级分类：General Economics 一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
--
一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
--
一级分类：Quantitative Finance 数量金融学
二级分类：Economics 经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
--
一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
--
---
英文摘要：
The steel industry has great impacts on the economy and the environment of both developed and underdeveloped countries. The importance of this industry and these impacts have led many researchers to investigate the relationship between a country's steel consumption and its economic activity resulting in the so-called intensity of use model. This paper investigates the validity of the intensity of use model for the case of Iran's steel consumption and extends this hypothesis by using the indexes of economic activity to model the steel consumption. We use the proposed model to train support vector machines and predict the future values for Iran's steel consumption. The paper provides detailed correlation tests for the factors used in the model to check for their relationships with the steel consumption. The results indicate that Iran's steel consumption is strongly correlated with its economic activity following the same pattern as the economy has been in the last four decades.
---
PDF链接：
https://arxiv.org/pdf/1912.02373
京公网安备 11010802022788号
