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.

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 = 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.