List of all decoder variables in model (excluding static variables)

Drops everything after the provided time `split_point` (excluded) from the index. Parameters ---------- index The index to d

Drops everything before the provided time `split_point` (excluded) from the index. Parameters ---------- index The index to

Determines the optimal alignment between two time series `series1` and `series2`, according to the Dynamic Time Warping algorithm. The al

The duration of the series (as a ``pandas.Timedelta`` or `int`).

Returns encoded index for inference/prediction. Parameters ---------- n The forecast horizon target

Returns encoded index for training. Parameters ---------- target The target TimeSeries used during training or pa

Returns encoded index for inference/prediction. Parameters ---------- n The forecast horizon target

Mapping between encoder identifier string (from parameters at model creations) and the corresponding future or past covariates encoder

List of all encoder variables in model (excluding static variables)

Returns the number of components generated by `SequentialEncoder.past_encoders` and `SequentialEncoder.future_encoders`.

The number of components in the `SingleEncoder` output.

Average the `forecasting_models` predictions, component-wise

Error (ERR). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per component/column, (o

Computes the anomaly score of the given time series, and returns the score of an agnostic threshold metric. Parameters ------

Compute the accuracy of the anomaly scores computed by the model. Predicts the `series` with the underlying forecasting/filtering model, and

Aggregates the (sequence of) multivariate series given as input into one (sequence of) series and evaluates the results against the ground tru

This function conditionally executes the given function with the given arguments based on whether the current level of 'logger' is below, abo

Explains a foreground time series, and returns a :class:`_ExplainabilityResult <darts.explainability.explainability_result._Explainab

Return year of time index entry, normalized

A seven element tuple containing in order: - min target lag - max target lag - min past covariate lag - max

F1 Score [1]_. For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per component/column a

Create a throwaway module that mimics a package ``__init__.py``.

Filters a given series Parameters ---------- series The series to filter. Returns -------

Returns the index of the first predicted within the output of self.model.

First value of the univariate series. Returns ------- float The first value of this univariate deterministic time

Fit the underlying forecasting/filtering model (if applicable) and the fittable scorers.

Fits/trains the model using the provided list of features time series and the target time series. Parameters ----------

Fit/train the model on (potentially multiple) series. Optionally, one or multiple past and/or future covariates series can be provided as wel

Fit/train the model on the (single) provided series. Optionally, a future covariates series can be provided as well. Parameters

Fits the model on the provided series. Note that `EnsembleModel.fit()` does NOT call `fit()` on each of its constituent forecasting m

Fits/trains the model using the provided list of features time series and the target time series. Parameters ----------

Fit/train the model on a (or potentially multiple) series. This method is only implemented for naive baseline models to provide a unified fit/

Fit/train the underlying forecasting model on (potentially multiple) series. Optionally, one or multiple past and/or future covariates series

Returns whether the `Encoder` object has been fitted.

Return whether the transformer has been fitted.

perform model training and prediction

Fit the transformer to the (sequence of) series and return the transformed input. Parameters ---------- series th

Returns whether the pipeline is fittable or not. A pipeline is fittable if at least one of the transformers in the pipeline is fittab

Add the parent path to pythonpath if current working dir is darts/examples

Fixture to fix the random state for tests.

Generates a JTensor of sinusoids with different frequencies.