TCNForecaster

class TCNForecaster(window, horizon=1, batch_size=32, n_epochs=100, verbose=0, optimizer=None, metrics='accuracy', loss='mse', callbacks=None, random_state=None, axis=0, last_file_name='last_model', save_best_model=False, file_path='./', n_blocks=None, kernel_size=2, dropout=0.2, save_last_model=False, save_init_model=False, best_file_name='best_model', init_file_name='init_model')

Bases: BaseDeepForecaster, IterativeForecastingMixin

A deep learning forecaster using Temporal Convolutional Network (TCN).

Adapted from the implementation used in [1]. Leverages the TCNNetwork from aeon’s network module to build the architecture suitable for forecasting tasks.

Parameters:

horizonint, default=1

Forecasting horizon, the number of steps ahead to predict.

batch_sizeint, default=32

Batch size for training the model.

n_epochsint, default=100

Number of epochs to train the model.

verboseint, default=0

Verbosity mode (0, 1, or 2).

optimizerstr or tf.keras.optimizers.Optimizer, default=None

Optimizer to use for training.

metricsstr or list[str|function|keras.metrics.Metric], default=”accuracy”

The evaluation metrics to use during training. Each can be a string, function, or a keras.metrics.Metric instance (see https://keras.io/api/metrics/). If a single string metric is provided, it will be used as the only metric. If a list of metrics are provided, all will be used for evaluation.

lossstr or tf.keras.losses.Loss, default=’mse’

Loss function for training.

callbackslist of tf.keras.callbacks.Callback or None, default=None

List of Keras callbacks to be applied during training.

random_stateint, default=None

Seed for random number generators.

axisint, default=0

Axis along which to apply the forecaster.

last_file_namestr, default=”last_model”

The name of the file of the last model, used for saving models.

save_best_modelbool, default=False

Whether to save the best model during training based on validation loss.

file_pathstr, default=”./”

Directory path where models will be saved.

n_blockslist of int, default=[16, 16, 16]

List specifying the number of output channels for each layer of the TCN. The length determines the depth of the network.

kernel_sizeint, default=2

Size of the convolutional kernel in the TCN.

dropoutfloat, default=0.2

Dropout rate applied after each convolutional layer for regularization.

save_last_modelbool, default=False

Whether or not to save the last model, last epoch trained.

save_init_modelbool, default=False

Whether to save the initialization of the model.

best_file_namestr, default=”best_model”

The name of the file of the best model.

init_file_namestr, default=”init_model”

The name of the file of the init model.

Notes

Capabilities

Missing Values	No
Multithreading	No
Univariate	Yes
Multivariate	No
Horizon	Yes
Exogenous	No

References

[1]

Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.

Methods

build_model(input_shape)	Build the TCN model for forecasting.
clone([random_state])	Obtain a clone of the object with the same hyperparameters.
fit(y[, exog, axis])	Fit forecaster to series y.
forecast(y[, exog, axis])	Forecast the next horizon steps ahead of y.
get_class_tag(tag_name[, raise_error, ...])	Get tag value from estimator class (only class tags).
get_class_tags()	Get class tags from estimator class and all its parent classes.
get_fitted_params([deep])	Get fitted parameters.
get_params([deep])	Get parameters for this estimator.
get_tag(tag_name[, raise_error, ...])	Get tag value from estimator class.
get_tags()	Get tags from estimator.
iterative_forecast(y, prediction_horizon[, exog])	Forecast prediction_horizon prediction using a single model fit on y.
load_model(model_path)	Load a pre-trained keras model instead of fitting.
predict(y[, exog, axis])	Predict the next horizon steps ahead.
reset([keep])	Reset the object to a clean post-init state.
save_last_model_to_file([file_path])	Save the last epoch of the trained deep learning model.
set_params(**params)	Set the parameters of this estimator.
set_tags(**tag_dict)	Set dynamic tags to given values.
summary()	Summary function to return the losses/metrics for model fit.

build_model(input_shape)

Build the TCN model for forecasting.

Parameters:

input_shapetuple

Shape of input data, typically (window, num_inputs).

Returns:

modeltf.keras.Model

Compiled Keras model with TCN architecture.

clone(random_state=None)

Obtain a clone of the object with the same hyperparameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self. Equal in value to type(self)(**self.get_params(deep=False)).

Parameters:

random_stateint, RandomState instance, or None, default=None

Sets the random state of the clone. If None, the random state is not set. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator.

Returns:

estimatorobject

Instance of type(self), clone of self (see above)

fit(y, exog=None, axis=1)

Fit forecaster to series y.

Fit a forecaster to predict self.horizon steps ahead using y.

Parameters:

ynp.ndarray

A time series on which to learn a forecaster to predict horizon ahead.

exognp.ndarray, default =None

Optional exogenous time series data assumed to be aligned with y.

Returns:

self

Fitted BaseForecaster.

forecast(y, exog=None, axis=1) → float

Forecast the next horizon steps ahead of y.

By default this is simply fit followed by predict.

Parameters:

ynp.ndarray

A time series to predict the next horizon value for. Must be of shape (n_channels, n_timepoints) if a multivariate time series.

exognp.ndarray, default =None

Optional exogenous time series data assumed to be aligned with y.

Returns:

float

single prediction self.horizon steps ahead of y.

classmethod get_class_tag(tag_name, raise_error=True, tag_value_default=None)

Get tag value from estimator class (only class tags).

Parameters:

tag_namestr

Name of tag value.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value

Value of the tag_name tag in cls. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError

if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> DummyClassifier.get_class_tag("capability:multivariate")
True

classmethod get_class_tags()

Get class tags from estimator class and all its parent classes.

Returns:

collected_tagsdict

Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance. These are not overridden by dynamic tags set by set_tags or class __init__ calls.

get_fitted_params(deep=True)

Get fitted parameters.

State required:

Requires state to be “fitted”.

Parameters:

deepbool, default=True

If True, will return the fitted parameters for this estimator and contained subobjects that are estimators.

Returns:

fitted_paramsdict

Fitted parameter names mapped to their values.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

get_tag(tag_name, raise_error=True, tag_value_default=None)

Get tag value from estimator class.

Includes dynamic and overridden tags.

Parameters:

tag_namestr

Name of tag to be retrieved.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value

Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError

if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> d.get_tag("capability:multivariate")
True

get_tags()

Get tags from estimator.

Includes dynamic and overridden tags.

Returns:

collected_tagsdict

Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance and then any overridden and new tags from __init__ or set_tags.

iterative_forecast(y, prediction_horizon, exog=None) → ndarray

Forecast prediction_horizon prediction using a single model fit on y.

This function implements the iterative forecasting strategy (also called recursive or iterated). This involves a single model fit on y which is then used to make prediction_horizon ahead forecasts using its own predictions as inputs for future forecasts. This is done by taking the prediction at step i and feeding it back into the model to help predict for step i+1. The basic contract of iterative_forecast is that fit is only ever called once.

ynp.ndarray

The time series to make forecasts about. Must be of shape (n_channels, n_timepoints) if a multivariate time series.

prediction_horizonint

The number of future time steps to forecast.

exognp.ndarray, default =None

Optional exogenous time series data assumed to be aligned with y.

Returns:

np.ndarray

An array of shape (prediction_horizon,) containing the forecasts for each horizon.

Raises:

ValueError

if prediction_horizon` less than 1.

Examples

>>> from aeon.forecasting import RegressionForecaster
>>> y = np.array([1.0, 2.0, 3.0, 4.0, 3.0, 2.0, 1.0, 2.0, 3.0, 4.0])
>>> f = RegressionForecaster(window=3)
>>> f.iterative_forecast(y,2)
array([3., 2.])

load_model(model_path)

Load a pre-trained keras model instead of fitting.

When calling this function, all functionalities can be used such as predict with the loaded model.

Parameters:

model_pathstr

Path to the saved model file including extension. Example: model_path=”path/to/file/best_model.keras”

Returns:

None

predict(y, exog=None, axis=1) → float

Predict the next horizon steps ahead.

Parameters:

ynp.ndarray

A time series to predict the next horizon value for.

exognp.ndarray, default =None

Optional exogenous time series data assumed to be aligned with y.

Returns:

float

single prediction self.horizon steps ahead of y.

reset(keep=None)

Reset the object to a clean post-init state.

After a self.reset() call, self is equal or similar in value to type(self)(**self.get_params(deep=False)), assuming no other attributes were kept using keep.

Detailed behaviour:

removes any object attributes, except:

hyper-parameters (arguments of __init__) object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyperparameters (result of get_params)

Not affected by the reset are:

object attributes containing double-underscores class and object methods, class attributes any attributes specified in the keep argument

Parameters:

keepNone, str, or list of str, default=None

If None, all attributes are removed except hyperparameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.

Returns:

selfobject

Reference to self.

Raises:

TypeError

If ‘keep’ is not a string or a list of strings.

save_last_model_to_file(file_path='./')

Save the last epoch of the trained deep learning model.

Parameters:

file_pathstr, default=”./”

The directory where the model will be saved.

Returns:

None

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_tags(**tag_dict)

Set dynamic tags to given values.

Parameters:

**tag_dictdict

Dictionary of tag name and tag value pairs.

Returns:

selfobject

Reference to self.

summary()

Summary function to return the losses/metrics for model fit.

Returns:

historydict or None

Dictionary containing model’s train/validation losses and metrics.