state_space_model¶

Classes

`CompositeSSM`(seasonal_ssms[, nonseasonal_ssm])	Class to compose several State Space Models.
`DaylySeasonalitySSM`()	Class for Daily Seasonality State Space Model.
`LevelSSM`()	Class for Level State Space Model.
`LevelTrendSSM`()	Class for Level-Trend State Space Model.
`SSM`()	Base class for State Space Model.
`SeasonalitySSM`(num_seasons, timestamp_transform)	Class for Seasonality State Space Model.
`WeeklySeasonalitySSM`()	Class for Weekly Seasonality State Space Model.
`YearlySeasonalitySSM`()	Class for Yearly Seasonality State Space Model.

class CompositeSSM(seasonal_ssms: List[etna.models.nn.deepstate.state_space_model.SeasonalitySSM], nonseasonal_ssm: Optional[Union[etna.models.nn.deepstate.state_space_model.LevelSSM, etna.models.nn.deepstate.state_space_model.LevelTrendSSM]] = None)[source]¶

Class to compose several State Space Models.

Create instance of CompositeSSM.

Parameters

seasonal_ssms (List[etna.models.nn.deepstate.state_space_model.SeasonalitySSM]) – List with the instances of Seasonality State Space Models.
nonseasonal_ssm (Optional[Union[etna.models.nn.deepstate.state_space_model.LevelSSM, etna.models.nn.deepstate.state_space_model.LevelTrendSSM]]) – Instance of Level or Level-Trend State Space Model.

emission_coeff(datetime_index: torch.Tensor) → torch.Tensor[source]¶

Emission coefficient matrix.

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

generate_datetime_index(timestamps: numpy.ndarray) → numpy.ndarray[source]¶

Generate datetime index to use in the State Space Model.

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

innovation_coeff(datetime_index: torch.Tensor) → torch.Tensor[source]¶

Innovation coefficient matrix.

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

latent_dim() → int[source]¶

Dimension of the latent space.

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

set_params(**params: dict) → etna.core.mixins.TMixin¶

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 – Estimator parameters
self (etna.core.mixins.TMixin) –
params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = 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()¶: Collect all information about etna object in dict.

transition_coeff(datetime_index: torch.Tensor) → torch.Tensor[source]¶

Transition coefficient matrix.

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

class DaylySeasonalitySSM[source]¶

Class for Daily Seasonality State Space Model.

Create instance of SeasonalitySSM.

Parameters: num_seasons – Number of seasons in the considered seasonality period.

emission_coeff(datetime_index: torch.Tensor) → torch.Tensor¶

Emission coefficient matrix.

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

generate_datetime_index(timestamps: numpy.ndarray) → numpy.ndarray¶

Generate datetime index to use in the State Space Model.

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

get_timestamp_transform(x: pandas._libs.tslibs.timestamps.Timestamp)[source]¶

Generate datetime index to use in the State Space Model.

Parameters: x (pandas._libs.tslibs.timestamps.Timestamp) – timestamp
Returns: Datetime index for State Space Model.

innovation_coeff(datetime_index: torch.Tensor) → torch.Tensor¶

Innovation coefficient matrix.

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

latent_dim() → int¶

Dimension of the latent space.

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

set_params(**params: dict) → etna.core.mixins.TMixin¶

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 – Estimator parameters
self (etna.core.mixins.TMixin) –
params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = 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()¶: Collect all information about etna object in dict.

transition_coeff(datetime_index: torch.Tensor) → torch.Tensor¶

Transition coefficient matrix.

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

class LevelSSM[source]¶

Class for Level State Space Model.

emission_coeff(datetime_index: torch.Tensor) → torch.Tensor[source]¶

Emission coefficient matrix.

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

generate_datetime_index(timestamps: numpy.ndarray) → numpy.ndarray[source]¶

Generate datetime index to use in the State Space Model.

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

innovation_coeff(datetime_index: torch.Tensor) → torch.Tensor[source]¶

Innovation coefficient matrix.

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

latent_dim() → int[source]¶

Dimension of the latent space.

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

set_params(**params: dict) → etna.core.mixins.TMixin¶

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 – Estimator parameters
self (etna.core.mixins.TMixin) –
params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = 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()¶: Collect all information about etna object in dict.

transition_coeff(datetime_index: torch.Tensor) → torch.Tensor[source]¶

Transition coefficient matrix.

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

class LevelTrendSSM[source]¶

Class for Level-Trend State Space Model.

emission_coeff(datetime_index: torch.Tensor) → torch.Tensor¶

Emission coefficient matrix.

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

generate_datetime_index(timestamps: numpy.ndarray) → numpy.ndarray¶

Generate datetime index to use in the State Space Model.

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

innovation_coeff(datetime_index: torch.Tensor) → torch.Tensor¶

Innovation coefficient matrix.

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

latent_dim() → int[source]¶

Dimension of the latent space.

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

set_params(**params: dict) → etna.core.mixins.TMixin¶

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 – Estimator parameters
self (etna.core.mixins.TMixin) –
params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = 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()¶: Collect all information about etna object in dict.

transition_coeff(datetime_index: torch.Tensor) → torch.Tensor[source]¶

Transition coefficient matrix.

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

class SSM[source]¶

Base class for State Space Model.

The system dynamics is described with the following equations:

\[y_t = a^T_t l_{t-1} + b_t + \sigma_t\varepsilon_t\]

\[l_t = F_t l_{t-1} + g_t\epsilon_t\]

\[l_0 \sim N(\mu_0, diag(\sigma_0^2)), \varepsilon_t \sim N(0, 1), \epsilon_t \sim N(0, 1),\]

where

\(y\) - state of the system

\(l\) - state of the system in the latent space

\(a\) - emission coefficient

\(F\) - transition coefficient

\(g\) - innovation coefficient

\(\sigma\) - noise standard deviation

\(\mu_0\) - prior mean

\(\sigma_0\) - prior standard deviation

abstract emission_coeff(datetime_index: torch.Tensor) → torch.Tensor[source]¶

Emission coefficient matrix.

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

abstract generate_datetime_index(timestamps: numpy.ndarray) → numpy.ndarray[source]¶

Generate datetime index to use in the State Space Model.

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

abstract innovation_coeff(datetime_index: torch.Tensor) → torch.Tensor[source]¶

Innovation coefficient matrix.

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

abstract latent_dim() → int[source]¶

Dimension of the latent space.

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

set_params(**params: dict) → etna.core.mixins.TMixin¶

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 – Estimator parameters
self (etna.core.mixins.TMixin) –
params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = 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()¶: Collect all information about etna object in dict.

abstract transition_coeff(datetime_index: torch.Tensor) → torch.Tensor[source]¶

Transition coefficient matrix.

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

class SeasonalitySSM(num_seasons: int, timestamp_transform: Callable[[pandas._libs.tslibs.timestamps.Timestamp], int])[source]¶

Class for Seasonality State Space Model.

Create instance of SeasonalitySSM.

Parameters

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

emission_coeff(datetime_index: torch.Tensor) → torch.Tensor[source]¶

Emission coefficient matrix.

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

generate_datetime_index(timestamps: numpy.ndarray) → numpy.ndarray[source]¶

Generate datetime index to use in the State Space Model.

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

innovation_coeff(datetime_index: torch.Tensor) → torch.Tensor¶

Innovation coefficient matrix.

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

latent_dim() → int[source]¶

Dimension of the latent space.

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

set_params(**params: dict) → etna.core.mixins.TMixin¶

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 – Estimator parameters
self (etna.core.mixins.TMixin) –
params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = 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()¶: Collect all information about etna object in dict.

transition_coeff(datetime_index: torch.Tensor) → torch.Tensor¶

Transition coefficient matrix.

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

class WeeklySeasonalitySSM[source]¶

Class for Weekly Seasonality State Space Model.

Create instance of SeasonalitySSM.

Parameters: num_seasons – Number of seasons in the considered seasonality period.

emission_coeff(datetime_index: torch.Tensor) → torch.Tensor¶

Emission coefficient matrix.

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

generate_datetime_index(timestamps: numpy.ndarray) → numpy.ndarray¶

Generate datetime index to use in the State Space Model.

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

get_timestamp_transform(x: pandas._libs.tslibs.timestamps.Timestamp)[source]¶

Generate datetime index to use in the State Space Model.

Parameters: x (pandas._libs.tslibs.timestamps.Timestamp) – timestamp
Returns: Datetime index for State Space Model.

innovation_coeff(datetime_index: torch.Tensor) → torch.Tensor¶

Innovation coefficient matrix.

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

latent_dim() → int¶

Dimension of the latent space.

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

set_params(**params: dict) → etna.core.mixins.TMixin¶

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 – Estimator parameters
self (etna.core.mixins.TMixin) –
params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = 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()¶: Collect all information about etna object in dict.

transition_coeff(datetime_index: torch.Tensor) → torch.Tensor¶

Transition coefficient matrix.

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

class YearlySeasonalitySSM[source]¶

Class for Yearly Seasonality State Space Model.

Create instance of SeasonalitySSM.

Parameters: num_seasons – Number of seasons in the considered seasonality period.

emission_coeff(datetime_index: torch.Tensor) → torch.Tensor¶

Emission coefficient matrix.

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

generate_datetime_index(timestamps: numpy.ndarray) → numpy.ndarray¶

Generate datetime index to use in the State Space Model.

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

get_timestamp_transform(x: pandas._libs.tslibs.timestamps.Timestamp)[source]¶

Generate datetime index to use in the State Space Model.

Parameters: x (pandas._libs.tslibs.timestamps.Timestamp) – timestamp
Returns: Datetime index for State Space Model.

innovation_coeff(datetime_index: torch.Tensor) → torch.Tensor¶

Innovation coefficient matrix.

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

latent_dim() → int¶

Dimension of the latent space.

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

set_params(**params: dict) → etna.core.mixins.TMixin¶

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 – Estimator parameters
self (etna.core.mixins.TMixin) –
params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = 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()¶: Collect all information about etna object in dict.

transition_coeff(datetime_index: torch.Tensor) → torch.Tensor¶

Transition coefficient matrix.

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