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_params`(**params)	Set the parameters of this estimator.
`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_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)[source]¶

Set dynamic tags to given values.

Parameters:

**tag_dictdict: Dictionary of tag name and tag value pairs.

Returns:

selfobject: Reference to self.