moving_average

Classes

MovingAverageModel([window])

MovingAverageModel averages previous series values to forecast future one.
class MovingAverageModel(window: int = 5)[source]

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

fit(ts: etna.datasets.tsdataset.TSDataset) → etna.models.seasonal_ma.SeasonalMovingAverageModel

Fit model.

For this model, fit does nothing.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with features

Returns

Model after fit

Return type

etna.models.seasonal_ma.SeasonalMovingAverageModel

forecast(ts: etna.datasets.tsdataset.TSDataset, prediction_size: int, return_components: bool = False) → etna.datasets.tsdataset.TSDataset

Make autoregressive forecasts.

Parameters

  • ts (etna.datasets.tsdataset.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

etna.datasets.tsdataset.TSDataset

get_model() → etna.models.seasonal_ma.SeasonalMovingAverageModel

Get internal model.

Returns

Itself

Return type

etna.models.seasonal_ma.SeasonalMovingAverageModel

classmethod load(path: pathlib.Path) → typing_extensions.Self

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 (pathlib.Path) – Path to load object from.

Returns

Loaded object.

Return type

typing_extensions.Self

params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution]

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, etna.distributions.distributions.BaseDistribution]

predict(ts: etna.datasets.tsdataset.TSDataset, prediction_size: int, return_components: bool = False) → etna.datasets.tsdataset.TSDataset

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

Parameters

  • ts (etna.datasets.tsdataset.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

etna.datasets.tsdataset.TSDataset

save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

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.

property context_size: int

Context size of the model.