hvandenb/Machine-Learning-in-the-Financial-Industry

In recent years the use of Deep Neural Networks (DNN) has become more prevalent and significant research has been done in this area. DNNs have different architectures to solve different Machine Learning (ML) problems. Breakthroughs in this field have been made due to the availability of data, and the ability to process a large amount of data. Datasets used in the financial industry leverage a wealth of time-series data. For each dataset classical statistical approaches have been used to interpret the data, or make predictions. These classical methods have not been able to address the multidimensionality of the data and had to rely on different approaches to correlate data. In this research, we compare a set of DNN architectures and their fit for use in the Financial Industry.

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