fusionlab.nn.components.MultiDecoder

class fusionlab.nn.components.MultiDecoder

Bases: Layer, NNLearner

MultiDecoder for multi-horizon forecasting [1].

This layer takes a single feature vector per example of shape \((B, F)\) and produces a separate output for each horizon step, resulting in \((B, H, O)\).

\[\mathbf{Y}_h = \text{Dense}_h(\mathbf{x}),\, h \in [1..H]\]

Each horizon has its own decoder layer.

Parameters:

• output_dim (int) – Number of output features for each horizon.

• num_horizons (int) – Number of forecast horizons.

Notes

This layer is particularly useful when you want separate parameters for each horizon, instead of a single shared head.

call(`x`, training=False)

Forward pass that produces horizon-specific outputs.

get_config()

Returns configuration for serialization.

from_config(`config`)

Builds a new instance from config.

Examples

>>> from fusionlab.nn.components import MultiDecoder
>>> import tensorflow as tf
>>> # Input of shape (batch_size, feature_dim)
>>> x = tf.random.normal((32, 128))
>>> # Instantiate multi-horizon decoder
>>> decoder = MultiDecoder(output_dim=1, num_horizons=3)
>>> # Output shape => (32, 3, 1)
>>> y = decoder(x)

See also

MultiModalEmbedding

Provides feature embeddings that can be fed into MultiDecoder.

QuantileDistributionModeling

Projects deterministic outputs into multiple quantiles per horizon.

References

[1]

Lim, B., & Zohren, S. (2021). “Time-series forecasting with deep learning: a survey.” Philosophical Transactions of the Royal Society A, 379(2194), 20200209.

__init__(output_dim, num_horizons)

Initialize the MultiDecoder.

Parameters:

• output_dim (int) – Number of features each horizon decoder should output.

• num_horizons (int) – Number of horizons to predict, each with its own Dense layer.

Methods

__init__(output_dim, num_horizons)	Initialize the MultiDecoder.
add_loss(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
add_metric(value[, name])	Adds metric tensor to the layer.
add_update(updates)	Add update op(s), potentially dependent on layer inputs.
add_variable(*args, **kwargs)	Deprecated, do NOT use! Alias for add_weight.
add_weight([name, shape, dtype, ...])	Adds a new variable to the layer.
build(input_shape)	Creates the variables of the layer (for subclass implementers).
build_from_config(config)	Builds the layer's states with the supplied config dict.
call(x[, training])	Forward pass: each horizon has a separate Dense layer.
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
count_params()	Count the total number of scalars composing the weights.
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)	Create a new MultiDecoder from the config.
get_build_config()	Returns a dictionary with the layer's input shape.
get_config()	Returns layer configuration for serialization.
get_input_at(node_index)	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at(node_index)	Retrieves the input shape(s) of a layer at a given node.
get_output_at(node_index)	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at(node_index)	Retrieves the output shape(s) of a layer at a given node.
get_params([deep])	Get the parameters for this learner.
get_weights()	Returns the current weights of the layer, as NumPy arrays.
help(**kwargs)
load(file_path[, format])	Load the learner's state from a specified file in the desired format.
load_own_variables(store)	Loads the state of the layer.
save([file_path, format, overwrite, ...])	Save the learner's state to a specified file in the desired format.
save_own_variables(store)	Saves the state of the layer.
set_params(**params)	Set the parameters of this learner.
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
summary()	Provide a summary of the learner's parameters.
with_name_scope(method)	Decorator to automatically enter the module name scope.

Attributes

activity_regularizer	Optional regularizer function for the output of this layer.
compute_dtype	The dtype of the layer's computations.
dtype	The dtype of the layer weights.
dtype_policy	The dtype policy associated with this layer.
dynamic	Whether the layer is dynamic (eager-only); set in the constructor.
inbound_nodes	Return Functional API nodes upstream of this layer.
input	Retrieves the input tensor(s) of a layer.
input_mask	Retrieves the input mask tensor(s) of a layer.
input_shape	Retrieves the input shape(s) of a layer.
input_spec	InputSpec instance(s) describing the input format for this layer.
losses	List of losses added using the add_loss() API.
metrics	List of metrics attached to the layer.
my_params
name	Name of the layer (string), set in the constructor.
name_scope	Returns a tf.name_scope instance for this class.
non_trainable_variables	Sequence of non-trainable variables owned by this module and its submodules.
non_trainable_weights	List of all non-trainable weights tracked by this layer.
outbound_nodes	Return Functional API nodes downstream of this layer.
output	Retrieves the output tensor(s) of a layer.
output_mask	Retrieves the output mask tensor(s) of a layer.
output_shape	Retrieves the output shape(s) of a layer.
stateful
submodules	Sequence of all sub-modules.
supports_masking	Whether this layer supports computing a mask using compute_mask.
trainable
trainable_variables	Sequence of trainable variables owned by this module and its submodules.
trainable_weights	List of all trainable weights tracked by this layer.
updates
variable_dtype	Alias of Layer.dtype, the dtype of the weights.
variables	Returns the list of all layer variables/weights.
weights	Returns the list of all layer variables/weights.

__init__(output_dim, num_horizons)

Initialize the MultiDecoder.

Parameters:

• output_dim (int) – Number of features each horizon decoder should output.

• num_horizons (int) – Number of horizons to predict, each with its own Dense layer.

call(x, training=False)

Forward pass: each horizon has a separate Dense layer.

Parameters:

• x (tf.Tensor) – A 2D tensor (B, F).

• training (bool, optional) – Unused in this layer. Defaults to False.

Returns:

A 3D tensor of shape (B, H, O).

Return type:

tf.Tensor

get_config()

Returns layer configuration for serialization.

Returns:

Dictionary containing ‘output_dim’ and ‘num_horizons’.

Return type:

dict

classmethod from_config(config)

Create a new MultiDecoder from the config.

Parameters:

config (dict) – Contains ‘output_dim’, ‘num_horizons’.

Returns:

A new instance.

Return type:

MultiDecoder

help(**kwargs)

my_params = MultiDecoder(output_dim, num_horizons)