deadline_ma

Classes

DeadlineMovingAverageModel([window, seasonality])

Moving average model that uses exact previous dates to predict.

SeasonalityMode(value)

Enum for seasonality mode for DeadlineMovingAverageModel.
class DeadlineMovingAverageModel(window: int = 3, seasonality: str = 'month')[source]

Moving average model that uses exact previous dates to predict.

Notes

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

Initialize deadline moving average model.

Length of the context is equal to the number of window months or years, depending on the seasonality.

Parameters

  • window (int) – Number of values taken for forecast for each point.

  • seasonality (str) – Only allowed values are “month” and “year”.

fit(ts: etna.datasets.tsdataset.TSDataset) → etna.models.deadline_ma.DeadlineMovingAverageModel[source]

Fit model.

Parameters

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

Returns

Model after fit

Return type

etna.models.deadline_ma.DeadlineMovingAverageModel

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

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 model isn’t fitted

  • ValueError: – if context isn’t big enough

  • ValueError: – if forecast context contains NaNs

Return type

etna.datasets.tsdataset.TSDataset

get_model() → etna.models.deadline_ma.DeadlineMovingAverageModel[source]

Get internal model.

Returns

Itself

Return type

etna.models.deadline_ma.DeadlineMovingAverageModel

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][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, etna.distributions.distributions.BaseDistribution]

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

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 model isn’t fitted

  • 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

Upper bound to context size of the model.