visualize
chronax.vis_forecast.visualize
visualize(forecasts, dataset, process_inputs=None, xlim=None, ylim=None, num_pictures=[0], verbose=True, fig_size=(10, 2))
| Parameter | Type | Default | Description |
|---|---|---|---|
forecasts |
- | - | list of forecast (SampleForecast, QuantileForecast, or list like iterables). expected shape of forecast[i] to be (num_samples, num_steps, num_variate) for gluonts sample object or (num_variate, num_steps) for numpy array |
dataset |
- | - | dataset object |
process_inputs |
- | None |
list of processed input samples |
xlim |
- | None |
tuple of x-axis limits, support only 1 element in the tuple as the left bound |
ylim |
- | None |
tuple of y-axis limits, support only 1 element in the tuple as the bottom bound |
num_pictures |
- | [0] |
list of indices of pictures to plot |
verbose |
- | True |
(undocumented) |
fig_size |
- | (10, 2) |
(undocumented) |
Returns: None (The function displays plots using matplotlib.pyplot.show()).