Long Short-Term Memory (LSTM) is a type of artificial neural network used in deep learning. LSTMs are a type of recurrent neural network (RNN) that is designed to process sequential data like speech, text, or time series data.
LSTMs are especially useful for tasks that require long-term memory retention, such as language translation, speech recognition and time series analysis. They function by utilizing a memory cell, which can store information for an extended period of time, as well as input and output gates, which control the flow of information into and out of the cell. These gates enable LSTMs to selectively remember or forget information, making them ideal for tasks requiring complex, long-term processing.
Because of their ability to handle sequential data more effectively than traditional neural networks, LSTMs have gained popularity in recent years, especially in trading. They have been used in a wide range of applications, including speech recognition, natural language processing, and image captioning.