ljungbox¶

In [ ]:

ljungbox(vdf,
         column: str,
         ts: str,
         by: list = [],
         p: int = 1,
         alpha: float = 0.05,
         box_pierce: bool = False)

Ljung–Box test (whether any of a group of autocorrelations of a time series are different from zero).

Parameters¶

Name	Type	Optional	Description
vdf	vDataFrame	❌	Input vDataFrame.
column	str	❌	Input vcolumn to test.
ts	str	❌	vcolumn used as timeline. It will be to use to order the data. It can be a numerical or type date like (date, datetime, timestamp...) vcolumn.
by	list	✓	vcolumns used in the partition.
p	int	✓	Number of lags to consider in the test.
alpha	float	✓	Significance Level. Probability to accept H0.
box_pierce	bool	✓	If set to True, the Box-Pierce statistic will be used.

Returns¶

tablesample : An object containing the result. For more information, see utilities.tablesample.

Example¶

In [103]:

from verticapy import *
texas = vDataFrame("Texas")
texas["cases"].plot(ts = "date")

In [104]:

from verticapy.stats import ljungbox
ljungbox(texas,
         column = "cases",
         ts = "date",
         p = 10)

Out[104]:

	Ljung–Box Test Statistic	p_value	Serial Correlation
1	91.03448275862068	1.411900511388355e-21	✅
2	183.12750601443463	1.7154075871800807e-40	✅
3	276.11771683796405	1.4650458360176346e-59	✅
4	370.02666540939265	8.303756428668113e-79	✅
5	464.8766808310794	3.033447199435367e-98	✅
6	560.6909091237624	6.997958362723855e-118	✅
7	657.2988735682068	1.103888634091634e-137	✅
8	754.7212095682067	1.174754870318247e-157	✅
9	852.780935137827	9.188103577980987e-178	✅
10	951.4962212916731	5.226090762139598e-198	✅

Rows: 1-10 | Columns: 4

In [105]:

# Box-Pierce
ljungbox(texas,
         column = "cases",
         ts = "date",
         p = 10,
         box_pierce = True)

Out[105]:

	Box-Pierce Test Statistic	p_value	Serial Correlation
1	88.0	6.545446540951261e-21	✅
2	176.0	6.054601895401188e-39	✅
3	263.824088	6.692083814228472e-57	✅
4	351.47244	8.43441460658278e-75	✅
5	438.94523200000003	1.1902907835209562e-92	✅
6	526.24264	1.863999540246192e-110	✅
7	613.1898080000001	3.5148375770453054e-128	✅
8	699.7874400000001	7.951961882367499e-146	✅
9	785.8620880000001	2.347224172641296e-163	✅
10	871.415336	9.018406318552994e-181	✅

Rows: 1-10 | Columns: 4