# Deep Learning with Time Series and Sequence Data

Create and train networks for time series classification, regression, and
forecasting tasks

Create and train networks for time series classification, regression, and forecasting tasks. Train long short-term memory (LSTM) networks for sequence-to-one or sequence-to-label classification and regression problems. You can train LSTM networks on text data using word embedding layers (requires Text Analytics Toolbox™) or convolutional neural networks on audio data using spectrograms (requires Audio Toolbox™).

## Apps

Deep Network Designer | Design, visualize, and train deep learning networks |

## Functions

## Blocks

## Properties

ConfusionMatrixChart Properties | Confusion matrix chart appearance and behavior |

## Topics

### Recurrent Networks

**Long Short-Term Memory Networks**

Learn about long short-term memory (LSTM) networks.**Time Series Forecasting Using Deep Learning**

This example shows how to forecast time series data using a long short-term memory (LSTM) network.**Sequence Classification Using Deep Learning**

This example shows how to classify sequence data using a long short-term memory (LSTM) network.**Sequence-to-Sequence Classification Using Deep Learning**

This example shows how to classify each time step of sequence data using a long short-term memory (LSTM) network.**Sequence-to-Sequence Regression Using Deep Learning**

This example shows how to predict the remaining useful life (RUL) of engines by using deep learning.**Sequence-to-One Regression Using Deep Learning**

This example shows how to predict the frequency of a waveform using a long short-term memory (LSTM) neural network.**Train Network with LSTM Projected Layer**

Train a deep learning network with an LSTM projected layer for sequence-to-label classification.**Create Simple Sequence Classification Network Using Deep Network Designer**

This example shows how to create a simple long short-term memory (LSTM) classification network using Deep Network Designer.**Classify Videos Using Deep Learning**

This example shows how to create a network for video classification by combining a pretrained image classification model and an LSTM network.**Classify Videos Using Deep Learning with Custom Training Loop**

This example shows how to create a network for video classification by combining a pretrained image classification model and a sequence classification network.**Image Captioning Using Attention**

This example shows how to train a deep learning model for image captioning using attention.**Train Network Using Custom Mini-Batch Datastore for Sequence Data**

This example shows how to train a deep learning network on out-of-memory sequence data using a custom mini-batch datastore.**Visualize Activations of LSTM Network**

This example shows how to investigate and visualize the features learned by LSTM networks by extracting the activations.**Chemical Process Fault Detection Using Deep Learning**

Use simulation data to train a neural network than can detect faults in a chemical process.**Create Simple Sequence Classification Network Using Deep Network Designer**

This example shows how to create a simple long short-term memory (LSTM) classification network using Deep Network Designer.**Train Latent ODE Network with Irregularly Sampled Time-Series Data**

This example shows how to train a latent ordinary differential equation (ODE) autoencoder with time-series data that is sampled at irregular time intervals.

### Convolutional Networks

**Sequence Classification Using 1-D Convolutions**

This example shows how to classify sequence data using a 1-D convolutional neural network.**Time Series Anomaly Detection Using Deep Learning**

This example shows how to detect anomalies in sequence or time series data.**Train Speech Command Recognition Model Using Deep Learning**

This example shows how to train a deep learning model that detects the presence of speech commands in audio.**Train Sequence Classification Network Using Data With Imbalanced Classes**

This example shows how to classify sequences with a 1-D convolutional neural network using class weights to modify the training to account for imbalanced classes.**Sequence-to-Sequence Classification Using 1-D Convolutions**

This example shows how to classify each time step of sequence data using a generic temporal convolutional network (TCN).**Train Network with Complex-Valued Data**

This example shows how to predict the frequency of a complex-valued waveform using a 1-D convolutional neural network.**Interpret Deep Learning Time-Series Classifications Using Grad-CAM**

This example shows how to use the gradient-weighted class activation mapping (Grad-CAM) technique to understand the classification decisions of a 1-D convolutional neural network trained on time-series data.**Sequence Classification Using CNN-LSTM Network**

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) layer.**Train Network with Numeric Features**

This example shows how to create and train a simple neural network for deep learning feature data classification.

### Deep Learning with Simulink

**Predict and Update Network State in Simulink**

This example shows how to predict responses for a trained recurrent neural network in Simulink® by using the`Stateful Predict`

block.**Classify and Update Network State in Simulink**

This example shows how to classify data for a trained recurrent neural network in Simulink® by using the`Stateful Classify`

block.**Predict Battery State of Charge Using Deep Learning**

This example shows how to train a neural network to predict the state of charge of a battery by using deep learning.**Improve Performance of Deep Learning Simulations in Simulink**

This example shows how to use code generation to improve the performance of deep learning simulations in Simulink®.**Physical System Modeling Using LSTM Network in Simulink**

This example shows how to create a reduced order model (ROM) to replace a Simscape component in a Simulink® model by training a long short-term memory (LSTM) neural network.

### Deep Learning with MATLAB

**List of Deep Learning Layers**

Discover all the deep learning layers in MATLAB^{®}.**Datastores for Deep Learning**

Learn how to use datastores in deep learning applications.**Deep Learning in MATLAB**

Discover deep learning capabilities in MATLAB using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.**Deep Learning Tips and Tricks**

Learn how to improve the accuracy of deep learning networks.**Data Sets for Deep Learning**

Discover data sets for various deep learning tasks.