On the fuzzification of the classical definition of preference structure
Fuzzy Sets and Systems
Neural Networks Finance and Investment: Using Artificial Intelligence to Improve Real-World Performance
Bionformatics Computing
Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Neural Computation
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Symbolic analysis of time series of economic indicators offers an advantage of transferring quantitative values into qualitative concepts by indexing a subset of intervals with a set of symbols. In a similar way, computer codes routinely process continuous problems in a discrete manner. This work explains an appealing analogy between the DNA code of life and the symbol series derived from financial markets. In particular, it is shown that similarity scoring schemes and the alignment gap concept known in bioinformatics have even more natural and deeper analogies in the economic systems. The symbolic analysis does not solely mean a loss of information; in also allows us to quantify a similarity degree between various financial time series (and their subsequences) in a rigorous way, which is a novel concept of practical importance in economic applications. Our symbolic analysis concept is illustrated by two types of market indicator series, namely the analysis of Dow Jones vs. NIKKEI 225 indices on one side, and the CZK/EUR exchange rate vs. Prague money market rates on the other side. The present framework may also yield a significantly reduced computational complexity as compared to the neural networks in the class of similarity-comparison algorithms.