国际国内甲醇市场分析及预测

介绍了近年来国际国内甲醇市场供需、生产、贸易、价格等变化特点，并对未来市场走向进行了分析预测。     

An explainable machine learning approach to improve take-off time predictions

Accurate aircraft trajectory predictions are necessary to compute exact traffic demand figures, which are crucial for an efficient and effective air traffic flow and capacity management. At present, the uncertainty of the take-off time is one of the major contributions to the loss of trajectory predictability. In the EUROCONTROL Maastricht Upper Area Control Centre, the predicted take-off time for each individual flight relies on the information received from the Enhanced Traffic Flow Management System. However, aircraft do not always take-off at the times reported by this system due to several factors, which effects and interactions are too complex to be expressed with hard-coded rules. Previous work proposed a machine learning model that, based on historical data, was able to predict the take-off time of individual flights from a set of input features that effectively captures some of these elements. The model demonstrated to reduce by 30% the take-off time prediction errors of the current system one hour before the time that flight is scheduled to depart from the parking position. This paper presents an extension of the model, which overcomes this look-ahead time constraint and allows to improve take-off time predictions as early as the initial flight plan is received. In addition, a subset of the original set of input features has been meticulously selected to facilitate the implementation of the solution in an operational air traffic flow and capacity management system, while minimising the loss of predictive power. Finally, the importance and interactions of the input features are thoroughly analysed with additive feature attribution methods.     

PRICING DERIVATIVES ON MULTISCALE DIFFUSIONS: AN EIGENFUNCTION EXPANSION APPROACH

Using tools from spectral analysis, singular and regular perturbation theory, we develop a systematic method for analytically computing the approximate price of a large class of derivative‐assets. The payoff of the derivative‐assets may be path‐dependent. In addition, the process underlying the derivatives may exhibit killing (i.e., jump to default) as well as combined local/nonlocal stochastic volatility. The nonlocal component of volatility may be multiscale, in the sense that it may be driven by one fast‐varying and one slow‐varying factor. The flexibility of our modeling framework is contrasted by the simplicity of our method. We reduce the derivative pricing problem to that of solving a single eigenvalue equation. Once the eigenvalue equation is solved, the approximate price of a derivative can be calculated formulaically. To illustrate our method, we calculate the approximate price of three derivative‐assets: a vanilla option on a defaultable stock, a path‐dependent option on a nondefaultable stock, and a bond in a short‐rate model.     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

基于SVM的房地产财务决策风险预测及敏感性分析

本文以房地产上市公司的财务数据为参考依据,结合相关性分析,筛选并建立影响财务风险的指标体系;利用支持向量机建立财务风险分析预测模型;并使用灰色关联理论对指标进行敏感性分析,从而得到影响财务风险最敏感的指标。     

对江苏省电力负荷进行回归预测

本文主要根据2001-2011年江苏省用电量样本数据,建立了江苏省电力负荷与人均GDP、工业化以及人口数之间的多元回归预测方程,并预测了江苏省2014-2020年总用电量数据,在此基础上提出了相应的建议。     

模糊层次分析法优化BP神经网络的交通冲突预测模型

本文通过全面剖析影响交通冲突的原因,以交通流量、道路几何设计和道路环境三方面的因素建立指标层次结构体系。提出基于模糊层次分析(FAHP)法优化BP神经网络(BPNN)的预测模型,应用于交通冲突预测。     

白音查干矿井涌水量预计

矿井涌水量计算是矿床水文地质勘查中一项重要而复杂的工作,也是矿床水文地质勘查中的根本任务之一。运用大井法和廊道法对首采区、首采工作面和开切眼涌水量进行了预计,认为大井法计算的涌水量成果较为可靠,矿井最大涌水量为296.80m3/h,为今后的矿井安全生产提供了理论依据。     

上海合作组织命运共同体:一项研究议题

构建上海合作组织命运共同体是中国政府提出的关于促进上海合作组织长远发展的重大倡议。由于上海合作组织命运共同体理念的提出时日尚短,学术界关于该理念的研究尚未全面启动。要使上海合作组织命运共同体理念成为一个有效的研究议题,不仅需要明晰上海合作组织命运共同体的具体内涵,而且需要将其纳入国际共同体、国际组织和大国外交等议题的研究,提炼出一些具有普遍性的理论命题。就攸关上海合作组织命运共同体构建的重大问题而言,至少需要回答上海合作组织命运共同体提出的理论与实践意义、上海合作组织命运共同体构建应遵循的基本路径、上海合作组织命运共同体构建的基础和条件、上海合作组织命运共同体构建面临的挑战与障碍、学术界能为上海合作组织命运共同体构建提供的对策建议等重大问题。在此过程中,学术界还需将其与周边命运共同体、人类命运共同体等家族相似性的概念纳入同一研究视域,以澄清上海合作组织命运共同体的内涵与外延,辨识上海合作组织命运共同体演变的动力与机制,并从理论层面提炼上海合作组织发展经验的特殊性与普遍性。     

The effects of handling outliers on the performance of bankruptcy prediction models

Ratio type financial indicators are the most popular explanatory variables in bankruptcy prediction models. These measures often exhibit heavily skewed distribution because of the presence of outliers. In the absence of clear definition of outliers, ad hoc approaches can be found in the literature for identifying and handling extreme values. However, it is not clear how these different approaches can affect the predictive power of models. There seems to be consensus in the literature on the necessity of handling outliers, at the same time, it is not clear how to define extreme values to be handled in order to maximize the predictive power of models. There are two possible ways to reduce the bias originating from outliers: omission and winsorization. Since the first approach has been examined previously in the literature, we turn our attention to the latter. We applied the most popular classification methodologies in this field: discriminant analysis, logistic regression, decision trees (CHAID and CART) and neural networks (multilayer perceptron). We assessed the predictive power of models in the framework of tenfold stratified crossvalidation and area under the ROC curve. We analyzed the effect of winsorization at 1, 3 and 5% and at 2 and 3 standard deviations, furthermore we discretized the range of each variable by the CHAID method and used the ordinal measures so obtained instead of the original financial ratios. We found that this latter data preprocessing approach is the most effective in the case of our dataset. In order to check the robustness of our results, we carried out the same empirical research on the publicly available Polish bankruptcy dataset from the UCI Machine Learning Repository. We obtained very similar results on both datasets, which indicates that the CHAID-based categorization of financial ratios is an effective way of handling outliers with respect to the predictive performance of bankruptcy prediction models.