LSTM (Long Short-Term Memory)

Information

• LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) architecture that is specifically designed to capture long-term dependencies and handle the problem of vanishing gradients that often occur in traditional RNNs.
• It was introduced in 1997 by Hochreiter and Schmidhuber to solve the problem of learning and remembering sequences of information for a long time.
• LSTM is based on the idea of a memory cell that can remember information for an extended period and a set of gates that regulate the flow of information into and out of the cell.
• The memory cell can selectively forget or remember information based on the input it receives and the state of the gates.
• The three main types of gates used in LSTM are the input gate, the output gate, and the forget gate.
• The input gate controls the flow of new information into the memory cell, the forget gate controls the flow of old information out of the cell, and the output gate controls the flow of the output from the cell.
• The LSTM architecture can be used for a variety of tasks, including speech recognition, natural language processing, and time series forecasting.
• LSTMs are particularly useful for time series forecasting because they can learn to capture long-term dependencies and exhibit stable convergence properties when trained with backpropagation through time.
• LSTMs have been shown to outperform other machine learning algorithms on a range of time series prediction tasks, including stock market prediction, energy demand forecasting, and weather forecasting.