Deep learning networks

aeon networks are the models behind the deep learning estimators, and can be used in

their own right to construct bespoke solutions.

BaseDeepLearningNetwork()	Abstract base class for deep learning networks.
TimeCNNNetwork([n_layers, kernel_size, ...])	Establish the network structure for a CNN.
EncoderNetwork([kernel_size, n_filters, ...])	Establish the network structure for an Encoder.
FCNNetwork([n_layers, n_filters, ...])	Establish the network structure for a FCN.
InceptionNetwork([n_filters, ...])	Inception Network.
MLPNetwork([n_layers, n_units, activation, ...])	Establish the network structure for a MLP.
ResNetNetwork([n_residual_blocks, ...])	Establish the network structure for a ResNet.
AEFCNNetwork([latent_space_dim, ...])	Establish the network structure for a AE-FCN.
AEResNetNetwork([latent_space_dim, ...])	Establish the network structure for a AE-ResNet.
LITENetwork([use_litemv, n_filters, ...])	LITE and LITE Multivariate (LITEMV) Networks.
AEBiGRUNetwork([latent_space_dim, n_layers, ...])	A class to implement an Auto-Encoder based on Bidirectional GRUs.
DisjointCNNNetwork([n_layers, n_filters, ...])	Establish the network structure for a DisjointCNN Network.
DCNNNetwork([latent_space_dim, n_layers, ...])	Establish the network structure for a DCNN-Model.
AEDCNNNetwork([latent_space_dim, ...])	Establish the Auto-Encoder based structure for a DCN Network.
AEAttentionBiGRUNetwork([latent_space_dim, ...])	A class to implement an Auto-Encoder based on Attention Bidirectional GRUs.
AEDRNNNetwork([latent_space_dim, ...])	Auto-Encoder based Dilated Recurrent Neural Networks (DRNN).
RecurrentNetwork([rnn_type, n_layers, ...])	Implements a Recurrent Neural Network (RNN) for time series forecasting.
NBeatsNetwork([horizon, stacks, ...])	Implementation of the N-BEATS network architecture.
TCNNetwork([n_blocks, kernel_size, dropout])	Temporal Convolutional Network (TCN) for sequence modeling.
DeepARNetwork([lstm_units, dense_units, dropout])	DeepAR Network for probabilistic time series forecasting.