Core Module Documentation

class neuralprophet.uncertainty.Conformal(alpha: Union[float, Tuple[float, float]], method: str, n_forecasts: int, quantiles: List[float])

Conformal prediction dataclass

Parameters

• alpha (float or tuple) – user-specified significance level of the prediction interval, float if coverage error spread arbitrarily over left and right tails, tuple of two floats for different coverage error over left and right tails respectively

• method (str) –

  name of conformal prediction technique used

  Options

  • naive: Naive or Absolute Residual

  • cqr: Conformalized Quantile Regression

• n_forecasts (int) – optional, number of steps ahead of prediction time step to forecast

• quantiles (list) – optional, list of quantiles for quantile regression uncertainty estimate

plot(plotting_backend=None)

Apply a given conformal prediction technique to get the uncertainty prediction intervals (or q-hats).

Parameters

plotting_backend (str) –

specifies the plotting backend for the nonconformity scores plot, if any

Options * plotly-resampler: Use the plotly backend for plotting in resample mode. This mode uses the

plotly-resampler package to accelerate visualizing large data by resampling it. For some environments (colab, pycharm interpreter) plotly-resampler might not properly vizualise the figures. In this case, consider switching to ‘plotly-auto’.

• plotly: Use the plotly backend for plotting

• matplotlib: use matplotlib for plotting

• (default) None: Plotting backend ist set automatically. Use plotly with resampling for jupyterlab

  notebooks and vscode notebooks. Automatically switch to plotly without resampling for all other environments.

predict(df: pandas.core.frame.DataFrame, df_cal: pandas.core.frame.DataFrame, show_all_PI: bool = False) → pandas.core.frame.DataFrame

Apply a given conformal prediction technique to get the uncertainty prediction intervals (or q-hat) for test dataframe.

Parameters

• df (pd.DataFrame) – test dataframe

• df_cal (pd.DataFrame) – calibration dataframe

• show_all_PI (bool) – whether to return all prediction intervals (including quantile regression and conformal prediction)

• Returns –

• ------- –

  pd.DataFrame

  test dataframe with uncertainty prediction intervals

neuralprophet.uncertainty.uncertainty_evaluate(df_forecast: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

Evaluate conformal prediction on test dataframe.

Parameters

df_forecast (pd.DataFrame) – forecast dataframe with the conformal prediction intervals

Returns

table containing evaluation metrics such as interval_width and miscoverage_rate

Return type

pd.DataFrame