adfuller¶

In [ ]:

adfuller(vdf,
         column: str,
         ts: str,
         by: list = [],
         p: int = 1, 
         with_trend: bool = False,
         regresults: bool = False)

Augmented Dickey-Fuller test (time series stationarity).

### 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.
with_trend	bool	✓	Adds a trend in the Regression.
regresults	bool	✓	If True, the full regression results are returned.

Returns¶

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

Example¶

In [91]:

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

In [92]:

from verticapy.stats import adfuller
adfuller(texas,
         column = "cases",
         ts = "date",
         p = 1,
         with_trend = True)

Out[92]:

	value
ADF Test Statistic	0.06343664217560875
p_value	0.949573193787402
# Lags used	1
# Observations Used	88
Critical Value (1%)	-4.04
Critical Value (2.5%)	-3.73
Critical Value (5%)	-5.45
Critical Value (10%)	-3.15
Stationarity (alpha = 1%)	❌

Rows: 1-9 | Columns: 2

In [94]:

# Result of the Regression
adfuller(texas,
         column = "cases",
         ts = "date",
         p = 5,
         regresults = True)

Out[94]:

	Abc predictor Varchar(65000)	123 coefficient Float	123 std_err Float	123 t_value Float	123 p_value Float
1	Intercept	35.1545462473338	24.7473008093382	1.42054062857912	0.159594434054043
2	lag1	0.000154850514105673	0.00321358170112057	0.0481862695607448	0.961695880521294
3	delta1	0.639530071451759	0.110338504021177	5.79607342989728	1.50637315783867e-07
4	delta2	0.0173218447011569	0.130538520171656	0.132695273995591	0.894789754969035
5	delta3	0.140349292772543	0.129956328004078	1.07997274875402	0.283615076813402
6	delta4	-0.231755664687906	0.130969497671048	-1.76953923477664	0.0808687521374672
7	delta5	0.419956434443623	0.113631727941533	3.69576738866193	0.000414483273968402

Rows: 1-7 | Columns: 5