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The success of machine learning models in the financial domain is highlyreliant on the quality of the data representation. In this paper, we focus onthe representation of limit order book data and discuss the opportunities andchallenges for learning representations of such data. We also experimentallyanalyse the issues associated with existing representations and present aguideline for future research in this area.

How Robust are Limit Order Book Representations under Data Perturbation?