NaiveModel

class NaiveModel(lag: int = 1)

Bases: etna.models.seasonal_ma.SeasonalMovingAverageModel

Naive model predicts t-th value of series with its (t - lag) value.

\[y_{t} = y_{t-s},\]

where \(s\) is lag.

Notes

This model supports in-sample and out-of-sample prediction decomposition. Prediction component here is the corresponding target lag.

Init NaiveModel.

Parameters

lag (int) – lag for new value prediction

Inherited-members

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

context_size

Context size of the model.

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

Get default grid for tuning hyperparameters.

This grid is empty.

Returns

Grid to tune.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]