NaiveForecaster

class NaiveForecaster(strategy='last', seasonal_period=1, horizon=1)[source]

Bases: BaseForecaster, DirectForecastingMixin, IterativeForecastingMixin

Naive forecaster with multiple strategies and flexible horizon.

Parameters:
strategystr, default=”last”

The forecasting strategy to use. Options: “last”, “mean”, “seasonal_last”.

  • “last” predicts the last value of the input series for all horizon steps.

  • “mean”: predicts the mean of the input series for all horizon steps.

  • “seasonal_last”: predicts the last season value in the training series. Returns np.nan if the effective seasonal data is empty.

seasonal_periodint, default=1

The seasonal period to use for the “seasonal_last” strategy. E.g., 12 for monthly data with annual seasonality.

horizonint, default =1

The number of time steps ahead to forecast. If horizon is one, the forecaster will learn to predict one point ahead. Only relevant for “seasonal_last”.

Methods

clone([random_state])

Obtain a clone of the object with the same hyperparameters.

direct_forecast(y, prediction_horizon[, exog])

Make prediction_horizon ahead forecasts using a fit for each horizon.

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.

predict(y[, exog, axis])

Predict the next horizon steps ahead.

reset([keep])

Reset the object to a clean post-init state.

set_fit_request(*[, axis, exog])

Configure whether metadata should be requested to be passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_predict_request(*[, axis, exog])

Configure whether metadata should be requested to be passed to the predict method.

set_tags(**tag_dict)

Set dynamic tags to given values.

Notes

Capabilities

Missing Values

No

Multithreading

No

Univariate

Yes

Multivariate

No

Horizon

Yes

Exogenous

No
clone(random_state=None)[source]

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)

direct_forecast(y, prediction_horizon, exog=None) → ndarray[source]

Make prediction_horizon ahead forecasts using a fit for each horizon.

This is commonly called the direct strategy. The forecaster is trained to predict one ahead, then retrained to fit two ahead etc. Not all forecasters are capable of being used with direct forecasting. The ability to forecast on horizons greater than 1 is indicated by the tag “capability:horizon”. If this tag is false this function raises a value error. This method cannot be overridden.

Parameters:
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 "capability:horizon is False or 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.direct_forecast(y,2)
array([3., 2.])
fit(y, exog=None, axis=1)[source]

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[source]

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)[source]

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()[source]

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)[source]

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)[source]

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()[source]

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[source]

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.])
predict(y, exog=None, axis=1) → float[source]

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)[source]

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.

set_fit_request(*, axis: bool | None | str = '$UNCHANGED$', exog: bool | None | str = '$UNCHANGED$') → NaiveForecaster

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:
axisstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for axis parameter in fit.

exogstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for exog parameter in fit.

Returns:
selfobject

The updated object.

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_predict_request(*, axis: bool | None | str = '$UNCHANGED$', exog: bool | None | str = '$UNCHANGED$') → NaiveForecaster

Configure whether metadata should be requested to be passed to the predict method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:
axisstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for axis parameter in predict.

exogstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for exog parameter in predict.

Returns:
selfobject

The updated object.

set_tags(**tag_dict)[source]

Set dynamic tags to given values.

Parameters:
**tag_dictdict

Dictionary of tag name and tag value pairs.

Returns:
selfobject

Reference to self.