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.     

Overcoming localization of knowledge — the role of professional service firms

The literature on organizational learning asserts that external learning is often limited geographically and technologically. We scrutinize to what extent organizations acquire external knowledge by accessing external knowledge repositories. We argue that professional service firms (PSFs) grant access to nonlocalized knowledge repositories and thereby not only facilitate external learning but also help to overcome localization. Focusing on patent law firms, we test our predictions using a unique dataset of 544,820 pairs of European patent applications. Analyzing patterns of knowledge flows captured in patent citations, we find that accessing a PSF's repository facilitates the acquisition of external knowledge. As the effect is more pronounced for knowledge that is distant to a focal organization, we conclude that having access to a knowledge repository compensates for localization disadvantages. Copyright © 2013 John Wiley & Sons, Ltd.     

Creating financial statements using FRx within Microsoft Dynamics GP: Learning vs. teaching

This project requires you to create financial statements using FRx within Microsoft Dynamics GP, an enterprise system. The project emphasizes the learner-centered paradigm rather than a teacher-centered educational paradigm. Researchers have found great importance in the learner-centered approach in educating students, especially in information systems (Landry, Saulnier, Wagner, & Longenecker, 2008; Saulnier, Landry, & Wagner, 2008). Major differences exist between the two approaches; for example, the professor gives information and evaluates, and emphasizes the right answer in a teacher-centered approach. The professor coaches and facilitates, and emphasizes that students generate good questions and learn from mistakes in a learner-centered approach. This project utilizes the learner-centered approach, an effective approach for you to use in today’s environment.     

Adaptive learning and distributional dynamics in an incomplete markets model

Recent research shows that several DSGE models provide a closer fit to the data under adaptive learning. This paper extends this research by introducing adaptive learning in the model of Krusell and Smith (1998) with uninsurable idiosyncratic risks and aggregate uncertainty. A first contribution of this paper establishes that the equilibrium of this framework is stable under least-squares learning. The second contribution consists of showing that bounded rationality enhances the ability of this model to match the distribution of income in the US. Learning increases significantly the Gini coefficients because of the opposite effects on consumption of the capital-rich and of the capital-poor agent. The third contribution is an empirical exercise that shows that learning can account for increases in the income Gini coefficient of up to 25% in a period of 28 years. Overall, these findings suggest that adaptive learning has important distributional repercussions in this class of models.     

Learning to bid in sequential Dutch auctions

We propose an agent-based computational model to investigate sequential Dutch auctions with particular emphasis on markets for perishable goods and we take as an example wholesale fish markets. Buyers in these markets sell the fish they purchase on a retail market. The paper provides an original model of boundedly rational behavior for wholesale buyers׳ behavior incorporating learning to improve profits, conjectures as to the bids that will be made and fictitious learning. We analyze the dynamics of the aggregate price under different market conditions in order to explain the emergence of market price patterns such as the well-known declining price paradox and the empirically observed fact that the very last transactions in the day may be at a higher price. The proposed behavioral model provides alternative explanations for market price dynamics to those which depend on standard hypotheses such as diminishing marginal profits. Furthermore, agents learn the option value of having the possibility of bidding in later rounds. When confronted with random buyers, such as occasional participants or new entrants, they learn to bid in the optimal way without being conscious of the strategies of the other buyers. When faced with other buyers who are also learning their behavior still displays some of the characteristics learned in the simpler case even though the problem is not analytically tractable.     

Learning,large deviations and rare events

We examine the role of generalized stochastic gradient constant gain (SGCG) learning in generating large deviations of an endogenous variable from its rational expectations value. We show analytically that these large deviations can occur with a frequency associated with a fat-tailed distribution even though the model is driven by thin-tailed exogenous stochastic processes. We characterize these large deviations, driven by sequences of consistently low or consistently high shocks and then apply our model to the canonical asset pricing framework. We demonstrate that the tails of the stationary distribution of the price–dividend ratio will follow a power law.     

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.     

关于对高职学生现用教材使用情况问卷调查的分析报告

本文分别从问卷调查的基本情况论述了高职学生现用教材使用情况问卷调查的分析报告。     

探究数字化时代对师范生信息素养的要求

本文从数字化时代发展的角度出发,指出数字化学习不仅是数字时代学习的重要方式,更是课程整合的核心,同时提高教师的信息素养是数字化学习的关键。随着信息社会的发展和中小学信息技术课的开展,对师范生的信息素养提出了更高的要求。目前师范生就业形式,信息素养状况怎样,存在哪些问题,怎样去解决这些问题提高自身的竞争力?这都是我们师范院校的学生所思考的问题。针对上述问题,本文提出了自己的观点和看法。     

终身学习视角下公共图书馆的转型与发展思考

公共图书馆作为公共文化服务体系的重要组成部分,在构建学习型社会的大背景下,为促进终身学习的理念,借鉴国外图书馆的前沿探索,提升人文素质和知识内涵,实现公共图书馆在新的历史形势下自身的转型与发展。