Biblio
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2016. Improving Classification of Patterns in Ultra-High Frequency Time Series with Evolutionary Algorithms. Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. :127–128.
This paper proposes a method of distinguishing stock market states, classifying them based on price variations of securities, and using an evolutionary algorithm for improving the quality of classification. The data represents buy/sell order queues obtained from rebuild order book, given as price-volume pairs. In order to put more emphasis on certain features before the classifier is used, we use a weighting scheme, further optimized by an evolutionary algorithm.