Sample uniformly between [0, 1] (for each batch example) and return the linear interpolation between the fitted quantiles closest to

symmetric Absolute Percentage Error (sAPE). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is comput

Saves the model under a given path or file handle. Example for saving and loading a :class:`SKLearnModel`: .. highlight

Saves the conformal model under a given path or file handle. Additionally, two files are stored if `self.model` is a `TorchForecasti

Computes the anomaly score on the two (sequence of) series. The function `diff_fn` passed as a parameter to the scorer, will transform `pred_

Whether any of the Scorers is trainable.

Whether any of the Scorers is univariate.

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

Setup test fixtures for TimeSeries representation tests.

The shape of the series `(n_timesteps, n_components, n_samples)`.

(batch, seq_len, inner_dim) -> (batch, n_heads, seq_len, kv_proj_dim)

Plot the results of the anomaly model. Computes the score on the given series input and shows the different anomaly scores with respect to ti

Plot the results of the anomaly model. Computes the score on the given series input and shows the different anomaly scores with respect to ti

Plot the results of the anomaly model. Computes the score on the given series input and shows the different anomaly scores with respect to ti

Squared Log Error (SLE). For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per componen

Squared Scaled Error (SSE) (see [1]_ for more information on scaled forecasting errors). It is the Squared Error (SE) scaled by the Mean SE (MSE)

The static covariates of this series. If defined, the static covariates are given as a `pandas.DataFrame`. The columns represent the static v

List of all static variables in model

Whether model supports future covariates

Whether model instance supports direct prediction of likelihood parameters

EnsembleModel can predict likelihood parameters if all its forecasting models were fitted with the same likelihood.

If available, uses `model`'s native multivariate support. If not available, obtains multivariate support by wrapping the univariate m

Whether the model considers more than one variate in the time series.

Whether the model supports optimized historical forecasts

Whether the model supports optimized historical forecasts

Whether the model supports optimized historical forecasts

Whether model supports past covariates