Pattern Mining in Ultra-High Frequency Order Books with Self-Organizing Maps
This paper addresses the issue of discovering frequent patterns in order book shapes, in the context of the stock market depth, for ultra-high frequency data. It proposes a computational intelligence approach to building frequent patterns by clustering order book shapes with Self-Organizing Maps. An experimental evaluation of the approach proposed on the London Stock Exchange Rebuild Order Book database succeeded with providing a number of characteristic shape patterns and also with estimating probabilities of some typical transitions between shape patterns in the order book.
KeywordsFrequent Pattern Pattern Mining Transition Probability Matrix Order Book Neighborhood Relation
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