sktime adapters (tsbootstrap.adapters)

sktime / skbase adapter classes over the functional bootstrap() core.

These are thin skbase.BaseObject estimators (discoverable by sktime and validated by check_estimator) that delegate to the pure functional core. The core stays stateless; all estimator state lives here, so non-sktime users and future accelerated backends use bootstrap() directly without the OOP layer.

class tsbootstrap.adapters.BaseTimeSeriesBootstrap(n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseObject

Base adapter: delegate generation to the functional bootstrap().

bootstrap(X: object, y: object | None = None, return_indices: bool = False) → Iterator[ndarray[tuple[int, ...], dtype[float64]]] | Iterator[tuple[ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[int64]] | None]]

Yield n_bootstraps bootstrap samples of X (optionally with indices).

get_n_bootstraps() → int

Number of bootstrap replicates this estimator generates.

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.IIDBootstrap(n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

i.i.d. resampling (baseline; assumes no serial dependence).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.MovingBlockBootstrap(block_length: int | Literal['auto'] = 'auto', n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Moving block bootstrap (overlapping fixed-length blocks).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.CircularBlockBootstrap(block_length: int | Literal['auto'] = 'auto', n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Circular block bootstrap (wrap-around blocks).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.StationaryBlockBootstrap(avg_block_length: int | Literal['auto'] = 'auto', n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Stationary bootstrap (Politis-Romano; geometric block lengths).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.NonOverlappingBlockBootstrap(block_length: int | Literal['auto'] = 'auto', n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Non-overlapping block bootstrap (Carlstein).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.TaperedBlockBootstrap(window: Literal['bartlett', 'blackman', 'hamming', 'hann', 'tukey'] = 'bartlett', block_length: int | Literal['auto'] = 'auto', alpha: float = 0.5, n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Tapered block bootstrap (energy-normalized window).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.ARResidualBootstrap(order: int = 1, n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Recursive AR residual bootstrap.

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.ARIMAResidualBootstrap(order: tuple[int, int, int] = (1, 1, 1), n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Recursive ARIMA residual bootstrap (differenced scale).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.VARResidualBootstrap(order: int = 1, n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Recursive VAR residual bootstrap (multivariate).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.SieveBootstrap(min_lag: int = 1, max_lag: int | None = None, criterion: Literal['aic', 'bic', 'hqic'] = 'bic', n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Sieve bootstrap (AR order selected on the original series).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

Concrete sktime/skbase bootstrap adapters over the functional core.

Each class is a thin skbase.BaseObject that stores its parameters, builds a MethodSpec, and delegates generation to tsbootstrap.bootstrap(). The shared base holds the delegation logic so the concrete classes carry only their parameters (shape “concrete over a shared base”).

class tsbootstrap.adapters.estimators.BaseTimeSeriesBootstrap(n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseObject

Base adapter: delegate generation to the functional bootstrap().

bootstrap(X: object, y: object | None = None, return_indices: bool = False) → Iterator[ndarray[tuple[int, ...], dtype[float64]]] | Iterator[tuple[ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[int64]] | None]]

Yield n_bootstraps bootstrap samples of X (optionally with indices).

get_n_bootstraps() → int

Number of bootstrap replicates this estimator generates.

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.estimators.IIDBootstrap(n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

i.i.d. resampling (baseline; assumes no serial dependence).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.estimators.MovingBlockBootstrap(block_length: int | Literal['auto'] = 'auto', n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Moving block bootstrap (overlapping fixed-length blocks).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.estimators.CircularBlockBootstrap(block_length: int | Literal['auto'] = 'auto', n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Circular block bootstrap (wrap-around blocks).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.estimators.StationaryBlockBootstrap(avg_block_length: int | Literal['auto'] = 'auto', n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Stationary bootstrap (Politis-Romano; geometric block lengths).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.estimators.NonOverlappingBlockBootstrap(block_length: int | Literal['auto'] = 'auto', n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Non-overlapping block bootstrap (Carlstein).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.estimators.TaperedBlockBootstrap(window: Literal['bartlett', 'blackman', 'hamming', 'hann', 'tukey'] = 'bartlett', block_length: int | Literal['auto'] = 'auto', alpha: float = 0.5, n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Tapered block bootstrap (energy-normalized window).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.estimators.ARResidualBootstrap(order: int = 1, n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Recursive AR residual bootstrap.

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.estimators.ARIMAResidualBootstrap(order: tuple[int, int, int] = (1, 1, 1), n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Recursive ARIMA residual bootstrap (differenced scale).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.estimators.VARResidualBootstrap(order: int = 1, n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Recursive VAR residual bootstrap (multivariate).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}

class tsbootstrap.adapters.estimators.SieveBootstrap(min_lag: int = 1, max_lag: int | None = None, criterion: Literal['aic', 'bic', 'hqic'] = 'bic', n_bootstraps: int = 999, random_state: int | None = None)

Bases: BaseTimeSeriesBootstrap

Sieve bootstrap (AR order selected on the original series).

classmethod get_test_params() → list[dict[str, Any]]

Return testing parameter settings for the skbase object.

get_test_params is a unified interface point to store parameter settings for testing purposes. This function is also used in create_test_instance and create_test_instances_and_names to construct test instances.

get_test_params should return a single dict, or a list of dict.

Each dict is a parameter configuration for testing, and can be used to construct an “interesting” test instance. A call to cls(**params) should be valid for all dictionaries params in the return of get_test_params.

The get_test_params need not return fixed lists of dictionaries, it can also return dynamic or stochastic parameter settings.

Parameters:

parameter_set (str, default="default") – Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

params – Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

Return type:

dict or list of dict, default = {}