Ralph Grothmann

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We introduce an explanatory multi-agent approach of multiple FX-market modeling based on neural networks. We consider the explicit and implicit dynamics of the market price. This paper extends previous work of modeling a single FX-market to an integrated approach, which allows one to treat several FX-markets simultaneously. Our approach is based on(More)
This paper deals with a neural network architecture which establishes a portfolio management system similar to the Black / Litterman approach. This allocation scheme distributes funds across various securities or financial markets while simultaneously complying with specific allocation constraints which meet the requirements of an investor. The portfolio(More)
Recurrent neural networks (RNNs) are typically considered as relatively simple architectures, which come along with complicated learning algorithms. This paper has a different view: We start from the fact that RNNs can model any high dimensional, nonlinear dynamical system. Rather than focusing on learning algorithms, we concentrate on the design of network(More)
M obile technology users often turn to location-based services and systems to get directions or information about their surrounding environment. Such technology presents various challenges, and research in different fields provides partial solutions to those needs: operations research solves the routing problem, machine learning addresses traffic(More)
In this paper we present the Urban Computing challenge and in particular we exemplify it in the context of traffic management. From our previous experiences in the field we draw requirements in terms of capacity to cope with heterogeneity in representation, semantics and defaults; with scale; with time-dependency of data; and with noisy, uncertain and(More)
Statistical relational learning analyzes the probabilistic constraints between the entities, their attributes and relationships. It represents an area of growing interest in modern data mining. Many leading researches are proposed with promising results. However, there is no easily applicable recipe of how to turn a relational domain (e.g. a database) into(More)