Abstract
Multi-temporal decision-making problems require information about the potential temporal trajectories of wind generation for a given time horizon. Typically, the Gaussian copula is used for modelling the dependency between probabilistic forecasts from different lead-times. This paper explores the vine copula framework as a benchmark model since it captures complex multivariate dependence structures with mixed types of dependencies. The results show that a Gaussian copula with a suitable covariance matrix suffice to generate high quality temporal trajectories.

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The ocean and the Blue Economy are increasingly top priorities worldwide. The immense ocean territory in the planet and its huge associated economical potential is envisioned to increase the activity at the ocean in the forthcoming years. The support of these activities, and the convergence to the Internet of Things paradigm, will demand wireless and mobile communications to connect humans and systems at remote ocean areas. Currently, there is no communications solution enabling cost-effective broadband Internet access at remote ocean areas in alternative to expensive, narrowband satellite communications. This paper presents the maritime communications solution being developed in the BLUECOM+ project. The BLUECOM+ solution enables cost-effective broadband Internet access at remote ocean areas using standard wireless access technologies, e.g., GPRS/UMTS/LTE and Wi-Fi. Its novelty lies on the joint use of TV white spaces for long range radio communications, tethered balloons for lifting communications nodes high above the ocean surface, multi-hop relaying techniques for radio range extension, and standard access networks at the ocean. Simulation results prove it is possible to reach radio ranges beyond 100 km and bitrates in excess of 3 Mbit/s using a two-hop land-sea communications chain.

Abstract
Data mining is one of the most effective methods for fraud detection. This is highlighted by 25% of organizations that have suffered from economic crimes [1]. This paper presents a case study using real-world data from a large retail company. We identify symptoms of fraud by looking for outliers. To identify the outliers and the context where outliers appear, we learn a regression tree. For a given node, we identify the outliers using the set of examples covered at that node, and the context as the conjunction of the conditions in the path from the root to the node. Surprisingly, at different nodes of the tree, we observe that some outliers disappear and new ones appear. From the business point of view, the outliers that are detected near the leaves of the tree are the most suspicious ones. These are cases of difficult detection, being observed only in a given context, defined by a set of rules associated with the node.

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