temporal_fusion_transformer_model: Temporal Fusion Transformer Module

In mlverse/tft: Implementation of Temporal Fusion Transformer

Temporal Fusion Transformer Module

Description

Temporal Fusion Transformer Module

Usage

temporal_fusion_transformer_model(
  num_features,
  feature_sizes,
  hidden_state_size = 100,
  dropout = 0.1,
  num_heads = 4,
  num_lstm_layers = 2,
  num_quantiles = 3
)

Arguments

num_features	a list containing the shapes for all necessary information to define the size of layers, including: - $encoder$past$(num|cat): shape of past features - $encoder$static$(num|cat): shape of the static features - $decoder$target: shape of the target variable We exclude the batch dimension.
feature_sizes	The number of unique elements for each categorical variable in the dataset.
hidden_state_size	The size of the model shared accross multiple parts of the architecture.
dropout	Dropout rate used in many different places in the network
num_heads	Number of heads in the attention layer.
num_lstm_layers	Number of LSTM layers used in the Locality Enhancement Layer. Usually 2 is good enough.
num_quantiles	the number of quantiles we are predicting for.

mlverse/tft documentation built on June 19, 2022, 4:31 a.m.