etna.models.nn.deepstate.SeasonalitySSM#

class SeasonalitySSM(num_seasons: int, timestamp_transform: Callable[[Timestamp], int])[source]#

Bases: LevelSSM

Class for Seasonality State Space Model.

Note

This class requires torch extension to be installed. Read more about this at installation page.

Create instance of SeasonalitySSM.

Parameters:

num_seasons (int) – Number of seasons in the considered seasonality period.
timestamp_transform (Callable[[Timestamp], int]) –

Methods

`emission_coeff`(datetime_index)	Emission coefficient matrix.
`generate_datetime_index`(timestamps)	Generate datetime index to use in the State Space Model.
`innovation_coeff`(datetime_index)	Innovation coefficient matrix.
`latent_dim`()	Dimension of the latent space.
`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.
`transition_coeff`(datetime_index)	Transition coefficient matrix.

Attributes

This class stores its __init__ parameters as attributes.

emission_coeff(datetime_index: Tensor) → Tensor[source]#

Emission coefficient matrix.

Parameters:: datetime_index (Tensor) – Tensor with the index values. Values should be from 0 to seasonal period.
Returns:: Emission coefficient matrix.
Return type:: Tensor

generate_datetime_index(timestamps: ndarray) → ndarray[source]#

Generate datetime index to use in the State Space Model.

Parameters:: timestamps (ndarray) – Array with timestamps.
Returns:: Datetime index for State Space Model.
Return type:: ndarray

innovation_coeff(datetime_index: Tensor) → Tensor[source]#

Innovation coefficient matrix.

Parameters:: datetime_index (Tensor) – Tensor with the index values. Values should be from 0 to seasonal period.
Returns:: Innovation coefficient matrix.
Return type:: Tensor

latent_dim() → int[source]#

Dimension of the latent space.

Returns:: Dimension of the latent space.
Return type:: int

set_params(**params: dict) → Self[source]#

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters:: **params (dict) – Estimator parameters
Returns:: New instance with changed parameters
Return type:: Self

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )

to_dict()[source]#: Collect all information about etna object in dict.

transition_coeff(datetime_index: Tensor) → Tensor[source]#

Transition coefficient matrix.

Parameters:: datetime_index (Tensor) – Tensor with the index values. Values should be from 0 to seasonal period.
Returns:: Transition coefficient matrix.
Return type:: Tensor