Model.plot¶

In [ ]:

Model.plot(vdf=None,
           X: list = [],
           ts: str = "",
           X_idx: int = 0,
           dynamic: bool = False,
           one_step: bool = True,
           observed: bool = True,
           confidence: bool = True,
           nlead: int = 10,
           nlast: int = 0,
           limit: int = 1000,
           ax=None,
           **style_kwds,)

Draws the model.

Parameters¶

Name	Type	Optional	Description
vdf	vDataFrame	✓	Object to use to run the prediction.
X	list	✓	List of the response columns.
ts	str	✓	vcolumn used to order the data.
X_idx	int/str	✓	Index of the main vector vcolumn to draw. It can also be the name of a predictor vcolumn.
dynamic	bool	✓	If set to True, the dynamic forecast will be drawn.
one_step	bool	✓	If set to True, the one step ahead forecast will be drawn.
observed	bool	✓	If set to True, the observation will be drawn.
confidence	bool	✓	If set to True, the confidence ranges will be drawn.
nlead	int	✓	Number of predictions computed by the dynamic forecast after the last ts date.
nlast	int	✓	The dynamic forecast will start nlast values before the last ts date.
limit	int	✓	Maximum number of past elements to use.
ax	Matplotlib axes object	✓	The axes to plot on.
**style_kwds	any	✓	Any optional parameter to pass to the Matplotlib functions.

Returns¶

ax : Matplotlib axes object

Example¶

In [2]:

from verticapy.learn.tsa import VAR
model = VAR(name = "VAR_Texas",
            p = 5)
model.drop()
model.fit(input_relation = "Texas", 
          X = ["cases", "deaths"],
          ts = "date")
model.plot(X_idx = "cases",
           nlead = 100,
           nlast = 30,
           dynamic = True)
model.plot(X_idx = "deaths",
           nlead = 100,
           nlast = 30,
           dynamic = True,)

Out[2]:

<AxesSubplot:title={'center':'VAR(5) ["deaths"]'}, xlabel='"date"'>