Abstract
In this paper we study methods for predicting the stock index DAX. The idea is to use the information provided by several dierent information sources. We consider two dierent types of information sources: 1. Human experts who formulate their knowledge in form of rules, and 2. Databases of objective measurable time series of nancial parameters. It is shown how to fuse these dierent types of knowledge by using neuro-fuzzy methods.We present experimental results that demonstrate the usefulness of these new concepts.
Cite
CITATION STYLE
Siekmann, S., Gebhardt, J., & Kruse, R. (1999). Information fusion in the context of stock index prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1638, pp. 363–373). Springer Verlag. https://doi.org/10.1007/3-540-48747-6_34
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