Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  concerns knowledge discovery from ultra-high frequency financial time series of order book data from the London Stock Exchange Rebuild Order Book database. It focuses on extracting frequent patterns from order book shapes, which characterize particular stock market states. Because order book shapes vary considerably in time, it is necessary to convert the original price-volume representation to real vectors of a fixed length.  two methods of representing order books are studied: using Gaussian Mixture Models (GMMs), and Gaussian density-based representation (GD). Both methods produce real vectors of a constant length and frequent patterns are defined as codebooks of clusters of these real vectors. To obtain useful knowledge  would like these patterns to represent different states of the market, be stable in time, and be universal.  define universality as periodical reoccurence, which makes historical knowledge usable for future decision making. Because of these time-related requirements a typical spatial clustering is not sufficient.  studies the usage of an evolutionary algorithm to obtain patterns with desirable properties.
['Krzysztof Michalak', 'Adrian Lancucki', 'Piotr Lipinski']
I am writing a research paper on a idea Multiobjective optimization of frequent pattern models in ultra-high frequency time series: Stability versus universality
[{"from": "human", "value": "I am writing a research paper on a idea Multiobjective optimization of frequent pattern models in ultra-high frequency time series: Stability versus universality"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  concerns knowledge discovery from ultra-high frequency financial time series of order book data from the London Stock Exchange Rebuild Order Book database. It focuses on extracting frequent patterns from order book shapes, which characterize particular stock market states. Because order book shapes vary considerably in time, it is necessary to convert the original price-volume representation to real vectors of a fixed length.  two methods of representing order books are studied: using Gaussian Mixture Models (GMMs), and Gaussian density-based representation (GD). Both methods produce real vectors of a constant length and frequent patterns are defined as codebooks of clusters of these real vectors. To obtain useful knowledge  would like these patterns to represent different states of the market, be stable in time, and be universal.  define universality as periodical reoccurence, which makes historical knowledge usable for future decision making. Because of these time-related requirements a typical spatial clustering is not sufficient.  studies the usage of an evolutionary algorithm to obtain patterns with desirable properties."}]