Chain-structured neural architecture search for financial time series forecasting

Deep neural networks have been very successful in a wide variety of tasks over the last two decades. In large part their success is attributed to their ability to perform very well without major manual feature engineering required when compared to more classical techniques. The hierarchical structure of the networks weighs and extracts important features automatically from the data during the learning process [GBC16]. However, the exact architecture of the neural network still has to be prescribed manually by the user. This led to the development of so-called auto-ML techniques that aim to automate this process. In the context of deep neural networks, auto-ML has a very large overlap with neural architecture search (NAS), itself having a large overlap with hyperparameter optimization.