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MOIRAIForecaster

MOIRAIForecaster

class MOIRAIForecaster(checkpoint_path: str, context_length=200, patch_size=32, num_samples=100, num_feat_dynamic_real=None, num_past_feat_dynamic_real=None, map_location=None, target_dim=2, broadcasting=False, deterministic=False, batch_size=32, use_source_package=False)[source]

MOIRAI Forecasters.

Parameters:
checkpoint_pathstr, default=None

Path to the checkpoint of the model. Supported weights are available at [1].

context_lengthint, default=200

Length of the context window, time points the model will take as input for inference.

patch_sizeint, default=32

Time steps to perform patching with.

num_samplesint, default=100

Number of samples to draw.

map_locationstr, default=None

Hardware to use for the model.

target_dimint, default=2

Dimension of the target.

deterministicbool, default=False

Whether to use a deterministic model.

batch_sizeint, default=32

Number of samples in each batch of inference.

broadcastingbool, default=False

if True, multiindex data input will be broadcasted to single series. For each single series, one copy of this forecaster will try to fit and predict on it. The broadcasting is happening inside automatically, from the outerside api perspective, the input and output are the same, only one multiindex output from predict use_source_package : bool, default=False If True, the model and configuration will be loaded directly from the source package uni2ts.models.moirai. This is useful if you want to bypass the local version of the package or when working in an environment where the latest updates from the source package are needed. If False, the model and configuration will be loaded from the local version of package maintained in sktime. To install the source package, follow the instructions here [2].

Attributes:
cutoff

Cut-off = “present time” state of forecaster.

fh

Forecasting horizon that was passed.

is_fitted

Whether fit has been called.

state

State of the estimator.

References

Examples

>>> from sktime.forecasting.moirai import MOIRAIForecaster
>>> import pandas as pd
>>> import numpy as np
>>> morai_forecaster = MOIRAIForecaster(
...     checkpoint_path="sktime/moirai-1.0-R-small"
... )
>>> y = np.random.normal(0, 1, (30, 2))
>>> X = y * 2 + np.random.normal(0, 1, (30,1))
>>> index = pd.date_range("2020-01-01", periods=30, freq="D")
>>> y = pd.DataFrame(y, index=index)
>>> X = pd.DataFrame(X, columns=["x1", "x2"], index=index)
>>> morai_forecaster.fit(y, X=X)
MOIRAIForecaster(checkpoint_path='sktime/moirai-1.0-R-small')
>>> X_test = pd.DataFrame(
...     np.random.normal(0, 1, (10, 2)),
...     columns=["x1", "x2"],
...     index=pd.date_range("2020-01-31", periods=10, freq="D"),
... )
>>> forecast = morai_forecaster.predict(fh=range(1, 11), X=X_test)

Methods

check_is_fitted([method_name])

Check if the estimator has been fitted.

check_period_index(df)

Check if the index is a PeriodIndex.

check_range_index(df)

Check if the index is a range index.

clone()

Obtain a clone of the object with same hyper-parameters and config.

clone_tags(estimator[, tag_names])

Clone tags from another object as dynamic override.

create_pandas_dataset(df, target[, ...])

Create a gluonts PandasDataset from the input data.

create_test_instance([parameter_set])

Construct an instance of the class, using first test parameter set.

create_test_instances_and_names([parameter_set])

Create list of all test instances and a list of names for them.

fit(y[, X, fh])

Fit forecaster to training data.

fit_predict(y[, X, fh, X_pred])

Fit and forecast time series at future horizon.

get_class_tag(tag_name[, tag_value_default])

Get class tag value from class, with tag level inheritance from parents.

get_class_tags()

Get class tags from class, with tag level inheritance from parent classes.

get_config()

Get config flags for self.

get_fitted_params([deep])

Get fitted parameters.

get_param_defaults()

Get object's parameter defaults.

get_param_names([sort])

Get object's parameter names.

get_params([deep])

Get a dict of parameters values for this object.

get_pretrained_params([deep])

Get pretrained parameters of this estimator.

get_tag(tag_name[, tag_value_default, ...])

Get tag value from instance, with tag level inheritance and overrides.

get_tags()

Get tags from instance, with tag level inheritance and overrides.

get_test_params([parameter_set])

Return testing parameter settings for the estimator.

handle_range_index(index)

Convert RangeIndex to Dummy DatetimeIndex.

infer_freq(index)

Infer frequency of the index.

is_composite()

Check if the object is composed of other BaseObjects.

load_from_path(serial)

Load object from file location.

load_from_serial(serial)

Load object from serialized memory container.

predict([fh, X])

Forecast time series at future horizon.

predict_interval([fh, X, coverage])

Compute/return prediction interval forecasts.

predict_proba([fh, X, marginal])

Compute/return fully probabilistic forecasts.

predict_quantiles([fh, X, alpha])

Compute/return quantile forecasts.

predict_residuals([y, X])

Return residuals of time series forecasts.

predict_var([fh, X, cov])

Compute/return variance forecasts.

pretrain(y[, X, fh])

Pre-train forecaster on panel (global) data.

reset()

Reset the object to a clean post-init state.

return_time_index(df)

Return the time index, given any type of index.

save([path, serialization_format])

Save serialized self to bytes-like object or to (.zip) file.

score(y[, X, fh])

Scores forecast against ground truth, using MAPE (non-symmetric).

set_config(**config_dict)

Set config flags to given values.

set_params(**params)

Set the parameters of this object.

set_random_state([random_state, deep, ...])

Set random_state pseudo-random seed parameters for self.

set_tags(**tag_dict)

Set instance level tag overrides to given values.

update(y[, X, update_params])

Update cutoff value and, optionally, fitted parameters.

update_predict(y[, cv, X, update_params, ...])

Make predictions and update model iteratively over the test set.

update_predict_single([y, fh, X, update_params])

Update model with new data and make forecasts.