Using string invariants for prediction searching for optimal parameters

Marek Bundzel, Tomáš Kasanický and Richard Pinčák

Physica A: Statistical Mechanics and its Applications, 2016, vol. 444, issue C, 680-688

Abstract: We have developed a novel prediction method based on string invariants. The method does not require learning but a small set of parameters must be set to achieve optimal performance. We have implemented an evolutionary algorithm for the parametric optimization. We have tested the performance of the method on artificial and real world data and compared the performance to statistical methods and to a number of artificial intelligence methods. We have used data and the results of a prediction competition as a benchmark. The results show that the method performs well in single step prediction but the method’s performance for multiple step prediction needs to be improved. The method works well for a wide range of parameters.

Keywords: String theory and string invariants; Evolutionary optimization; Artificial intelligence (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:444:y:2016:i:c:p:680-688

DOI: 10.1016/j.physa.2015.10.050

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