Long Short-Term Memory Neural Networks This topic explains how to work with sequence and time series data for classification and regression tasks using long short-term memory (LSTM) …

A long short-term memory (LSTM) network is a type of recurrent neural network (RNN). LSTMs are predominantly used to learn, process, and classify sequential data because they can learn …

An LSTM layer is an RNN layer that learns long-term dependencies between time steps in time-series and sequence data.

Time Series Forecasting Using Deep Learning This example shows how to forecast time series data using a long short-term memory (LSTM) network. An LSTM network is a recurrent neural …

The long short-term memory (LSTM) operation allows a network to learn long-term dependencies between time steps in time series and sequence data.

This example shows how to classify sequence data using a long short-term memory (LSTM) network. To train a deep neural network to classify sequence data, you can use an LSTM …

This example shows how to create a 2-D CNN-LSTM network for speech classification tasks by combining a 2-D convolutional neural network (CNN) with a long short-term memory (LSTM) …

This example trains a sequence-to-one regression LSTM network using the Waveform data set, which contains 1000 synthetically generated waveforms of varying lengths with three …

Get Started with Time Series Forecasting This example shows how to create a simple long short-term memory (LSTM) network to forecast time series data using the Deep Network Designer …

This example shows how to use Long Short-Term Memory (LSTM) neural networks to estimate a linear system and compares this approach to transfer-function estimation.