Recurrent Neural Network

Architecture

Recurrent Neural Networks (RNNs) are a class of neural networks designed to process sequential data, a characteristic particularly valuable in cryptocurrency markets where time series analysis is paramount. Their core innovation lies in incorporating feedback loops, enabling the network to maintain a ‘memory’ of past inputs, influencing the processing of subsequent data points. This architecture contrasts with feedforward networks, which treat each input independently. Within options trading and financial derivatives, this memory allows for modeling dependencies across time, crucial for predicting price movements and volatility surfaces.