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复杂系统Complex Systems是指由于其各部分之间或某一系统与其环境之间的依赖、竞争、关系或其他类型的相互作用，其行为在本质上难以建模。复杂 “的系统具有从这些关系中产生的独特属性，如非线性、涌现、自发秩序、适应和反馈回路等等。由于这类系统出现在各种各样的领域，它们之间的共性已经成为其独立的研究领域的主题。在许多情况下，将这样的系统表示为一个网络是很有用的，其中节点代表组件，链接代表它们的相互作用。
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物理代写|复杂系统作业代写Complex Systems代考|Methodology
The research variables are the following:
Dependent variable is business growth which is measured by the increase in the accounting account called sales revenue.
Independent variables:
(1) Investments are equal to the accounts of total assets, current assets, inventory, property, branch, and equipment.
(2) Financing is equal to the accounts of total liabilities, current liabilities, consolidated net worth. Two indicators or ratios were also calculated to measure the indebtedness, with the formulas: Total liabilities/Total assets and Consolidated equity/Total liabilities.
(3) Profitability is equal to the net profit, EBIT, and EBITDA accounts. Likewise, the applied profitability ratios were: Net income/consolidated equity or stockholders’ equity, EBIT/Total assets, EBIT/Consolidated equity, EBIT/Capex, EBIT/Capital employed, EBITDA/Consolidated equity, EBITDA/Total assets.
The growth rates were calculated as follows: $g=(($ data $2009-$ data 2008)/data $2008) * 100$ and so on until 2017 .
The calculation of the Information Value (IV) was done with the statistical tool $\mathrm{R}$ (2018) and the woeBinning package (Eichenberg 2018). With the selected variables, predictive models were generated with a dependent variable called growth. This variable indicates whether a company grew or not with respect to the previous year. Growth is defined as true if the sales index is bigger than zero; otherwise, it is said that there is no growth.
物理代写|复杂系统作业代写Complex Systems代考|Analysis and Presentation of Results
From the dataset described above, we proceeded to calculate the Information Value of each of the index variables. Since the dataset has records marked NA (not available) that prevent the correct calculation of the IV, a first step was the elimination of all records with NAs. So before calculating the IV of each variable, all the NAs were eliminated. We proceeded this way for each variable independently. Variables showing a high correlation index were also eliminated; although some variables such as EBITDA have a high information value ( $0.237)$, they were not selected because they are correlated with others (in this case EBIT).
From this initial processing, it was determined that the variables with the highest information value were EBIT, consolidated net assets, and total assets. With values of $0.318,0.194$, and $0.180$, respectively, it is indicated that the interval from $0.3$ to $0.5$ means strong and from $0.1$ to $0.3$ is medium. Once these three variables were selected, we proceeded to the detection and elimination of data with atypical values (outliers). Once the atypical data were eliminated, the three predictive models were constructed: logistic regression, neural networks, and support vector machines. They were constructed indicating that growth should be predicted (objective variable) using EBIT, consolidated net assets, and total assets as predictors (independent variables). Figure 1 shows the ROC curve, and it is observed that in red, the behavior of a classifier based on an artificial neural network is shown, in green, the logistic regression model, and in blue color, the model that uses support vector machines.
复杂系统代写
物理代写|复杂系统作业代写COMPLEX SYSTEMS代考|METHODOLOGY
研究变量如下： 因
变量是业务增长，通过称为销售收入的会计科目的增加来衡量。
自变量：
1投资等于总资产、流动资产、存货、财产、分支机构和设备的账户。
2融资等于总负债、流动负债、合并净值的账目。还计算了两个指标或比率来衡量债务，公式为：总负债/总资产和合并权益/总负债。
3盈利能力等于净利润、EBIT 和 EBITDA 账户。同样，应用的盈利比率为：净收入/合并股权或股东权益、息税前利润/总资产、息税前利润/合并股权、息税前利润/资本支出、息税前利润/使用资本、息税折旧摊销前利润/合并股权、息税前利润/总资产。
增长率计算如下：G=((数据2009−数据 2008) / 数据2008)∗100以此类推，直到 2017 年。
信息价值的计算一世五用统计工具完成R 2018和 woeBinning 包和一世CH和nb和rG2018. 使用选定的变量，使用称为增长的因变量生成预测模型。该变量表示公司相对于上一年是否增长。如果销售指数大于零，则增长定义为真；否则，就说没有增长。
物理代写|复杂系统作业代写COMPLEX SYSTEMS代考|ANALYSIS AND PRESENTATION OF RESULTS
从上述数据集中，我们开始计算每个指标变量的信息值。由于数据集的记录标记为 NAn这吨一种v一种一世一世一种b一世和为了防止正确计算 IV，第一步是消除所有带有 NA 的记录。所以在计算每个变量的 IV 之前，所有的 NA 都被消除了。我们以这种方式独立地处理每个变量。显示高相关指数的变量也被消除；尽管 EBITDA 等一些变量具有很高的信息价值$0.237$，他们没有被选中，因为他们与其他人相关一世n吨H一世sC一种s和和乙一世吨.
从这个初始处理中，确定具有最高信息价值的变量是 EBIT、合并净资产和总资产。与价值0.318,0.194， 和0.180，分别表示从0.3到0.5意味着强大和来自0.1到0.3是中等的。一旦选择了这三个变量，我们就开始检测和消除具有非典型值的数据这你吨一世一世和rs. 一旦消除了非典型数据，就构建了三个预测模型：逻辑回归、神经网络和支持向量机。它们的构造表明应该预测增长这bj和C吨一世v和v一种r一世一种b一世和使用 EBIT、合并净资产和总资产作为预测变量一世nd和p和nd和n吨v一种r一世一种b一世和s. 图 1 显示了 ROC 曲线，可以观察到，红色表示基于人工神经网络的分类器的行为，绿色表示逻辑回归模型，蓝色表示使用支持向量机的模型。
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