etna.models.MovingAverageModel#

class MovingAverageModel(window: int = 5)[source]#

Bases: SeasonalMovingAverageModel

MovingAverageModel averages previous series values to forecast future one.

\[y_{t} = \frac{\sum_{i=1}^{n} y_{t-i} }{n},\]

where \(n\) is window size.

Notes

This model supports in-sample and out-of-sample prediction decomposition. Prediction components are corresponding target lags with weights of \(1/window\).

Init MovingAverageModel.

Parameters:: window (int) – number of history points to average

Methods

`fit`(ts)	Fit model.
`forecast`(ts, prediction_size[, ...])	Make autoregressive forecasts.
`get_model`()	Get internal model.
`load`(path)	Load an object.
`params_to_tune`()	Get default grid for tuning hyperparameters.
`predict`(ts, prediction_size[, return_components])	Make predictions using true values as autoregression context (teacher forcing).
`save`(path)	Save the object.
`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.

Attributes

This class stores its __init__ parameters as attributes.

context_size

Context size of the model.

fit(ts: TSDataset) → SeasonalMovingAverageModel[source]#

Fit model.

For this model, fit does nothing.

Parameters:: ts (TSDataset) – Dataset with features
Returns:: Model after fit
Return type:: SeasonalMovingAverageModel

forecast(ts: TSDataset, prediction_size: int, return_components: bool = False) → TSDataset[source]#

Make autoregressive forecasts.

Parameters:

ts (TSDataset) – Dataset with features
prediction_size (int) – Number of last timestamps to leave after making prediction. Previous timestamps will be used as a context.
return_components (bool) – If True additionally returns forecast components

Returns:

Dataset with predictions

Raises:

NotImplementedError: – if return_components mode is used
ValueError: – if context isn’t big enough
ValueError: – if forecast context contains NaNs

Return type:

TSDataset

get_model() → SeasonalMovingAverageModel[source]#

Get internal model.

Returns:: Itself
Return type:: SeasonalMovingAverageModel

classmethod load(path: Path) → Self[source]#

Load an object.

Warning

This method uses dill module which is not secure. It is possible to construct malicious data which will execute arbitrary code during loading. Never load data that could have come from an untrusted source, or that could have been tampered with.

Parameters:: path (Path) – Path to load object from.
Returns:: Loaded object.
Return type:: Self

params_to_tune() → Dict[str, BaseDistribution][source]#

Get default grid for tuning hyperparameters.

This grid tunes window parameter. Other parameters are expected to be set by the user.

Returns:: Grid to tune.
Return type:: Dict[str, BaseDistribution]

predict(ts: TSDataset, prediction_size: int, return_components: bool = False) → TSDataset[source]#

Make predictions using true values as autoregression context (teacher forcing).

Parameters:

ts (TSDataset) – Dataset with features
prediction_size (int) – Number of last timestamps to leave after making prediction. Previous timestamps will be used as a context.
return_components (bool) – If True additionally returns prediction components

Returns:

Dataset with predictions

Raises:

NotImplementedError: – if return_components mode is used
ValueError: – if context isn’t big enough
ValueError: – if forecast context contains NaNs

Return type:

TSDataset

save(path: Path)[source]#

Save the object.

Parameters:: path (Path) – Path to save object to.

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.

property context_size: int[source]#: Context size of the model.