LSTM Architectures

Long Short-Term Memory, or LSTM, is a type of recurrent neural network architecture capable of learning long-term dependencies in data. Unlike standard neural networks, LSTMs have internal mechanisms called gates that regulate the flow of information, allowing them to remember important patterns over long periods.

This makes them exceptionally well-suited for financial time series forecasting where past events have lasting impacts on future prices. In crypto, LSTMs are used to model the complex, multi-scale dependencies of price movements.

They can process sequences of historical returns, volume, and other metrics to generate accurate volatility forecasts. By maintaining a memory of past market regimes, they outperform simpler models in volatile environments.

They are a cornerstone of modern deep learning applications in quantitative finance.