Ralph Grothmann

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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)
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)
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)
The popularity of location-based services and automotive navigation systems calls for a new generation of intelligent solutions to support users in mobility. This article presents a traffic-aware semantic routing service for mobile users based on the Large Knowledge Collider (LarKC) Semantic Web pluggable platform. It proposes a technique for integrating(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)
This paper introduces a stock-picking algorithm that can be used to perform an optimal asset allocation for a large number of investment opportunities. The allocation scheme is based upon the idea of causal risk. Instead of referring to the volatility of the assets time series, the stock-picking algorithm determines the risk exposure of the portfolio by(More)
Fuzzy c-neural network models (FCNNM) combine clustering techniques with advanced neural networks for time series modeling in order to make predictions for a possibly large set of time series using only a small number of models. Given a set of time series, FCNNM finds a partition matrix that quantifies to which degree each time series is associated with(More)