Abstract
In this paper we study methods for predicting the stock index DAX. The idea is to use the information provided by several different information sources. We consider two different types of information sources: 1. Human experts who formulate their knowledge in form of rules, and 2. Databases of objective measurable time series of financial parameters. It is shown how to fuse these different types of knowledge by using neuro-fuzzy methods.We present experimental results that demonstrate the usefulness of these new concepts.
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Siekmann, S., Gebhardt, J., Kruse, R. (1999). Information Fusion in the Context of Stock Index Prediction. In: Hunter, A., Parsons, S. (eds) Symbolic and Quantitative Approaches to Reasoning and Uncertainty. ECSQARU 1999. Lecture Notes in Computer Science(), vol 1638. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48747-6_34
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DOI: https://doi.org/10.1007/3-540-48747-6_34
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