Machine learning modelling of temporal enterprise data

In a few words, this is a fintech data analysis project. The idea is, given temporal data of financial nature, to build algorithms that predict its evolution over time. For instance, the data could be certain assets prices, or customer buying history. The objective would be to respectively predict this asset price in a close future, or to anticipate what the customer wants to buy next and make relevant recommendations. Such algorithms belong to the family of machine learning algorithms. In the last years, machine learning has been of keen interest to the worldwide scientific community, as a sub-branch of machine learning named deep learning has seen considerable algorithmic progress. This project would leverage recent and efficient deep learning methods.

Faculty Supervisor:

David Duvenaud


Mathieu Ravaut


Layer 6 AI


Computer science


Information and communications technologies




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