vDataFrame.acf¶

In [ ]:

vDataFrame.acf(column: str,
               ts: str,
               by: list = [],
               p=12,
               unit: str = "rows",
               method: str = "pearson",
               acf_type: str = "bar",
               confidence: bool = True,
               alpha: float = 0.95,
               round_nb: int = 3,
               show: bool = True,
               ax = None,
               **style_kwds,)

Computes the correlations of the input vcolumn and its lags.

Parameters¶

Name	Type	Optional	Description
ts	str	❌	TS (Time Series) vcolumn to use to order the data. It can be of type date or a numerical vcolumn.
column	str	❌	Input vcolumn to use to compute the Auto Correlation Plot.
by	list	✓	vcolumns used in the partition.
p	int / list	✓	If int: the maximum number of lag to consider during the computation. If List: a list of the different lags to include in the computation. p must be positive or a list of positive integers.
unit	str	✓	Unit to use to compute the lags. rows : Natural lags. else : Any time unit. For example: 'hour' computes the hour lags and 'day' to computes the day lags.
method	str	✓	Method to use to compute the correlation. pearson : Pearson correlation coefficient (linear). spearmann : Spearmann's rank correlation coefficient (monotonic - rank based). kendall : Kendall rank correlation coefficient (similar trends). The method will compute the Tau-B coefficient. ⚠ Warning: This method is computationally expensive; it uses a CROSS JOIN during computation, the complexity of which is O(n * n), where n is the total count of the vDataFrame. cramer : Cramer's V (correlation between categories). biserial : Point-biserial (correlation between binaries and a numericals).
acf_type	str	✓	ACF Type. bar : Classical Autocorrelation Plot using bars. heatmap : Draws the ACF heatmap. line : Draws the ACF using a Line Plot.
confidence	bool	✓	If set to True, the confidence band width is drawn.
alpha	float	✓	Significance Level. Probability to accept H0. Only used to compute the confidence band width.
round_nb	int	✓	Round the coefficient using the input number of digits. It is used only if acf_type is 'heatmap'.
show	bool	✓	If set to True, the Auto Correlation Plot will be drawn using Matplotlib.
ax	Matplotlib axes object	✓	The axes to plot on.
**style_kwds	any	✓	Any optional parameter to pass to the Matplotlib functions.

Returns¶

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

Example¶

In [61]:

from verticapy.datasets import load_amazon
amazon = load_amazon()
display(amazon)

	📅 date Date	Abc state Varchar(32)	123 number Int
1	1998-01-01	ACRE	0
2	1998-01-01	ALAGOAS	0
3	1998-01-01	AMAPÁ	0
4	1998-01-01	AMAZONAS	0
5	1998-01-01	BAHIA	0
6	1998-01-01	CEARÁ	0
7	1998-01-01	DISTRITO FEDERAL	0
8	1998-01-01	ESPÍRITO SANTO	0
9	1998-01-01	GOIÁS	0
10	1998-01-01	MARANHÃO	0
11	1998-01-01	MATO GROSSO	0
12	1998-01-01	MATO GROSSO DO SUL	0
13	1998-01-01	MINAS GERAIS	0
14	1998-01-01	PARANÁ	0
15	1998-01-01	PARAÍBA	0
16	1998-01-01	PARÁ	0
17	1998-01-01	PERNAMBUCO	0
18	1998-01-01	PIAUÍ	0
19	1998-01-01	RIO DE JANEIRO	0
20	1998-01-01	RIO GRANDE DO NORTE	0
21	1998-01-01	RIO GRANDE DO SUL	0
22	1998-01-01	RONDÔNIA	0
23	1998-01-01	RORAIMA	0
24	1998-01-01	SANTA CATARINA	0
25	1998-01-01	SERGIPE	0
26	1998-01-01	SÃO PAULO	0
27	1998-01-01	TOCANTINS	0
28	1998-02-01	ACRE	0
29	1998-02-01	ALAGOAS	0
30	1998-02-01	AMAPÁ	0
31	1998-02-01	AMAZONAS	0
32	1998-02-01	BAHIA	0
33	1998-02-01	CEARÁ	0
34	1998-02-01	DISTRITO FEDERAL	0
35	1998-02-01	ESPÍRITO SANTO	0
36	1998-02-01	GOIÁS	0
37	1998-02-01	MARANHÃO	0
38	1998-02-01	MATO GROSSO	0
39	1998-02-01	MATO GROSSO DO SUL	0
40	1998-02-01	MINAS GERAIS	0
41	1998-02-01	PARANÁ	0
42	1998-02-01	PARAÍBA	0
43	1998-02-01	PARÁ	0
44	1998-02-01	PERNAMBUCO	0
45	1998-02-01	PIAUÍ	0
46	1998-02-01	RIO DE JANEIRO	0
47	1998-02-01	RIO GRANDE DO NORTE	0
48	1998-02-01	RIO GRANDE DO SUL	0
49	1998-02-01	RONDÔNIA	0
50	1998-02-01	RORAIMA	0
51	1998-02-01	SANTA CATARINA	0
52	1998-02-01	SERGIPE	0
53	1998-02-01	SÃO PAULO	0
54	1998-02-01	TOCANTINS	0
55	1998-03-01	ACRE	0
56	1998-03-01	ALAGOAS	0
57	1998-03-01	AMAPÁ	0
58	1998-03-01	AMAZONAS	0
59	1998-03-01	BAHIA	0
60	1998-03-01	CEARÁ	0
61	1998-03-01	DISTRITO FEDERAL	0
62	1998-03-01	ESPÍRITO SANTO	0
63	1998-03-01	GOIÁS	0
64	1998-03-01	MARANHÃO	0
65	1998-03-01	MATO GROSSO	0
66	1998-03-01	MATO GROSSO DO SUL	0
67	1998-03-01	MINAS GERAIS	0
68	1998-03-01	PARANÁ	0
69	1998-03-01	PARAÍBA	0
70	1998-03-01	PARÁ	0
71	1998-03-01	PERNAMBUCO	0
72	1998-03-01	PIAUÍ	0
73	1998-03-01	RIO DE JANEIRO	0
74	1998-03-01	RIO GRANDE DO NORTE	0
75	1998-03-01	RIO GRANDE DO SUL	0
76	1998-03-01	RONDÔNIA	0
77	1998-03-01	RORAIMA	0
78	1998-03-01	SANTA CATARINA	0
79	1998-03-01	SERGIPE	0
80	1998-03-01	SÃO PAULO	0
81	1998-03-01	TOCANTINS	0
82	1998-04-01	ACRE	0
83	1998-04-01	ALAGOAS	0
84	1998-04-01	AMAPÁ	0
85	1998-04-01	AMAZONAS	0
86	1998-04-01	BAHIA	0
87	1998-04-01	CEARÁ	0
88	1998-04-01	DISTRITO FEDERAL	0
89	1998-04-01	ESPÍRITO SANTO	0
90	1998-04-01	GOIÁS	0
91	1998-04-01	MARANHÃO	0
92	1998-04-01	MATO GROSSO	0
93	1998-04-01	MATO GROSSO DO SUL	0
94	1998-04-01	MINAS GERAIS	0
95	1998-04-01	PARANÁ	0
96	1998-04-01	PARAÍBA	0
97	1998-04-01	PARÁ	0
98	1998-04-01	PERNAMBUCO	0
99	1998-04-01	PIAUÍ	0
100	1998-04-01	RIO DE JANEIRO	0

Rows: 1-100 | Columns: 3

In [62]:

# Autocorrelation Plot for each 'month' lag using Spearman coefficients
# p = 48: it will compute 48 'months' lags
amazon.acf(ts = "date", 
           column = "number", 
           p = 48,
           by = ["state"],
           unit = "month",
           method = "spearman")

Out[62]:

	value	confidence
0	1	0.024396841824873748
1	0.86543680396017	0.03856205308291557
2	0.641922639518878	0.04656468707262692
3	0.39938929350142	0.052519441106804815
4	0.185655799846523	0.057377036550587374
5	0.0459383199230746	0.061640765754674524
6	-0.0093684335920518	0.0655069183976806
7	0.0196168076254727	0.06894501806026314
8	0.127468215578189	0.07161118776156336
9	0.296828511393074	0.07416885585834694
10	0.494663471420921	0.07659027318891991
11	0.669183623721803	0.07887501028576362
12	0.756240777751227	0.08070972531360529
13	0.703656422347367	0.08246563028583806
14	0.545077794147088	0.08399101248988675
15	0.343092597277807	0.08547965236320183
16	0.154975502588019	0.0868462967771087
17	0.0229978976509497	0.08794223779603168
18	-0.0400085787092103	0.08899807709603456
19	-0.023663217087369	0.08984615265637208
20	0.0671919278641314	0.09063193558306873
21	0.215056031781125	0.09126725326056165
22	0.394326540990952	0.0918613330687679
23	0.560174388414002	0.0924395780522622
24	0.65232694499396	0.09293318667622075
25	0.62236564761964	0.09323727861686824
26	0.489241759585553	0.09351085391986542
27	0.30934414528762	0.09373816688889977
28	0.130889801728171	0.09389849041590317
29	-0.00131802232447298	0.09401990717406696
30	-0.069982748843881	0.09413611707174371
31	-0.0700269423512902	0.09424661024229615
32	0.00470065681955768	0.09433115129623874
33	0.133914615889873	0.09440975398732615
34	0.29999224404642	0.0944787937500136
35	0.460378217949214	0.09451720691829557
36	0.558670337789043	0.0945555772769078
37	0.552691671399349	0.09459152125235575
38	0.443166080540354	0.09461816517092184
39	0.274713391212048	0.09464457943128332
40	0.106021727769531	0.09466530944697167
41	-0.0229982303581731	0.09468281718440483
42	-0.0986764341222668	0.0946937428701469
43	-0.110317490821392	0.09470447438051062
44	-0.054853876630662	0.09471520792593062
45	0.0551848919438232	0.09472503189708015
46	0.203993285445905	0.09473297816528793
47	0.35497398680027	0.09474051064208233
48	0.458820853220682	0.09474791601815644

Rows: 1-49 | Columns: 3

In [63]:

# Autocorrelation Plot using only the selected lags
amazon.acf(ts = "date", 
           column = "number", 
           by = ["state"],
           p = [1, 3, 6, 7],
           unit = "year",
           method = "pearson")

Out[63]:

	value	confidence
0	1.0	0.024396841824873748
1	0.616	0.03235856951582219
3	0.477	0.036306700072106235
6	0.395	0.03878403253532033
7	0.367	0.04080280865931269

Rows: 1-5 | Columns: 3

In [64]:

# Autocorrelation Heatmap for each 'month' lag
amazon.acf(ts = "date", 
           column = "number", 
           by = ["state"],
           p = 12,
           unit = "month",
           method = "pearson", 
           round_nb = 3, 
           acf_type = "heatmap")

Out[64]:

	"number"
"number"	1.0
"lag_12_number"	0.778
"lag_1_number"	0.752
"lag_11_number"	0.577
"lag_2_number"	0.422
"lag_10_number"	0.334
"lag_3_number"	0.201
"lag_9_number"	0.154
"lag_6_number"	-0.06
"lag_4_number"	0.052
"lag_7_number"	-0.043
"lag_5_number"	-0.033
"lag_8_number"	0.027

Rows: 1-13 | Columns: 2

In [65]:

# Autocorrelation Line for each 'month' lag
amazon.acf(ts = "date", 
           column = "number", 
           by = ["state"],
           p = 12,
           unit = "month",
           method = "pearson",
           acf_type = "line")

Out[65]:

	value	confidence
0	1.0	0.024396841824873748
1	0.752	0.03627598368700659
2	0.422	0.04460428511364169
3	0.201	0.0488503589603483
4	0.052	0.05097893985732261
5	-0.033	0.05227002803589448
6	-0.06	0.05273251493184901
7	-0.043	0.05300388580086683
8	0.027	0.053048454728269544
9	0.154	0.053082940352639066
10	0.334	0.05310781786440197
11	0.577	0.05312416251240187
12	0.778	0.053136467322383245

Rows: 1-13 | Columns: 3

vDataFrame.asfreq	Interpolates and computes a regular time interval vDataFrame.
vDataFrame.corr	Computes the Correlation Matrix of a vDataFrame.
vDataFrame.cov	Computes the Covariance Matrix of the vDataFrame.
vDataFrame.pacf	Computes the Partial Autocorrelations of the input vcolumn.

vDataFrame.acf¶

Parameters¶

Returns¶

Example¶

See Also¶