Core Module Documentation#

neuralprophet.plot_forecast.plot(fcst, quantiles, ax=None, xlabel='ds', ylabel='y', highlight_forecast=None, line_per_origin=False, figsize=(10, 6))#

Plot the NeuralProphet forecast

Parameters:
  • fcst (pd.DataFrame) – Output of m.predict

  • quantiles (list) – Quantiles for which the forecasts are to be plotted

  • ax (matplotlib axes) – Axes to plot on

  • xlabel (str) – Label name on X-axis

  • ylabel (str) – Label name on Y-axis

  • highlight_forecast (int) – i-th step ahead forecast to highlight.

  • line_per_origin (bool) – Print a line per forecast of one per forecast age

  • figsize (tuple) – Width, height in inches.

Returns:

Figure showing the NeuralProphet forecast

Examples

Base usage

>>> from neuralprophet import NeuralProphet
>>> m = NeuralProphet()
>>> metrics = m.fit(df, freq="D")
>>> future = m.make_future_dataframe(df=df, periods=365)
>>> forecast = m.predict(df=future)
>>> fig_forecast = m.plot(forecast)

Additional plot specifications

>>> fig_forecast = m.plot(forecast,
>>>                       xlabel="ds",
>>>                       ylabel="y",
>>>                       highlight_forecast=None,
>>>                       line_per_origin=False,
>>>                       figsize=(10, 6)
>>>                       )

Return type:

matplotlib.pyplot.figure

neuralprophet.plot_forecast.plot_components(m, fcst, df_name='__df__', quantile=0.5, forecast_in_focus=None, one_period_per_season=True, residuals=False, figsize=None)#

Plot the NeuralProphet forecast components.

Parameters:
  • m (NeuralProphet) – Fitted model

  • fcst (pd.DataFrame) – Output of m.predict

  • df_name (str) – ID from time series that should be plotted

  • quantile (float) – Quantile for which the forecast components are to be plotted

  • forecast_in_focus (int) – n-th step ahead forecast AR-coefficients to plot

  • one_period_per_season (bool) – Plot one period per season, instead of the true seasonal components of the forecast.

  • residuals (bool) – Flag whether to plot the residuals or not.

  • figsize (tuple) –

    Width, height in inches.

    Note

    Default value is set to None -> automatic figsize = (10, 3 * npanel)

Returns:

Figure showing the NeuralProphet forecast components

Return type:

matplotlib.pyplot.figure

neuralprophet.plot_forecast.plot_forecast_component(fcst, comp_name, plot_name=None, ax=None, figsize=(10, 6), multiplicative=False, bar=False, rolling=None, add_x=False, fill=False)#

Plot a particular component of the forecast.

Parameters:
  • fcst (pd.DataFrame) – Output of m.predict

  • comp_name (str) – Name of the component to plot

  • plot_name (str) – Name of the plot Title

  • ax (matplotlib axis) – Matplotlib Axes to plot on

  • figsize (tuple) –

    Width, height in inches. Ignored if ax is not None

    Note

    Default value is set to figsize = (10, 6)

  • multiplicative (bool) – Set y axis as percentage

  • bar (bool) – Make barplot

  • rolling (int) – Rolling average underplot

  • add_x (bool) – Add x symbols to plotted points

  • fill (bool) – Add fill between signal and x(y=0) axis

Returns:

List of Artist objects containing a particular forecast component

Return type:

matplotlib.artist.Artist

neuralprophet.plot_forecast.plot_multiforecast_component(fcst, comp_name, plot_name=None, ax=None, figsize=(10, 6), multiplicative=False, bar=False, focus=1, num_overplot=None)#

Plot a particular component of the forecast.

Parameters:
  • fcst (pd.DataFrame) – Output of m.predict.

  • comp_name (str) – Name of the component to plot.

  • plot_name (str) – Name of the plot Title.

  • ax (matplotlib axis) – Matplotlib Axes to plot on.

  • figsize (tuple) –

    Width, height in inches, ignored if ax is not None.

    Note

    Default value is set to figsize = (10, 6)

  • multiplicative (bool) – Set y axis as percentage

  • bar (bool) – Make barplot

  • focus (int) – Forecast number to portray in detail.

  • num_overplot (int) –

    Overplot all forecasts up to num

    Note

    Default value is set to num_overplot = None -> only plot focus

Returns:

List of Artist objects containing a particular forecast component

Return type:

matplotlib.artist.Artist