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verticapy.machine_learning.model_selection.hp_tuning.plot_acf_pacf

verticapy.machine_learning.model_selection.hp_tuning.plot_acf_pacf(vdf: vDataFrame, column: str, ts: str, by: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, p: int | list = 15, show: bool = True, **style_kwargs) → TableSample

Draws the ACF and PACF Charts.

Parameters

vdf: vDataFrame

Input vDataFrame.

column: str

Response column.

ts: str

vDataColumn used as timeline to order the data. It can be a numerical or date-like type (date, datetime, timestamp…) vDataColumn.

by: list, optional

vDataColumns used in the partition.

p: int | list, optional

Integer equal to the maximum number of lags to consider during the computation or a list of the different lags to include during the computation. p must be positive or a list of positive integers.

show: bool, optional

If set to True, the Plotting object is returned.

**style_kwargs

Any optional parameter to pass to the Plotting functions.

Returns

TableSample

acf, pacf, confidence

Examples

We import verticapy:

import verticapy as vp

Hint

By assigning an alias to verticapy, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from verticapy are used as intended without interfering with functions from other libraries.

For this example, we will use the amazon dataset.

import verticapy.datasets as vpd

amazon = vpd.load_amazon()

	📅 date Date	Abc state Varchar(32)	123 number Integer
1	1998-01-01	AMAPÁ	0
2	1998-01-01	AMAZONAS	0
3	1998-01-01	DISTRITO FEDERAL	0
4	1998-01-01	ESPÍRITO SANTO	0
5	1998-01-01	MARANHÃO	0
6	1998-01-01	PARANÁ	0
7	1998-01-01	PIAUÍ	0
8	1998-01-01	RORAIMA	0
9	1998-01-01	SERGIPE	0
10	1998-01-01	SÃO PAULO	0
11	1998-02-01	GOIÁS	0
12	1998-02-01	MATO GROSSO DO SUL	0
13	1998-02-01	MINAS GERAIS	0
14	1998-02-01	PARAÍBA	0
15	1998-02-01	SANTA CATARINA	0
16	1998-02-01	SÃO PAULO	0
17	1998-03-01	AMAPÁ	0
18	1998-03-01	BAHIA	0
19	1998-03-01	MATO GROSSO DO SUL	0
20	1998-03-01	PARÁ	0
21	1998-03-01	PERNAMBUCO	0
22	1998-03-01	RIO GRANDE DO SUL	0
23	1998-04-01	CEARÁ	0
24	1998-04-01	PARANÁ	0
25	1998-04-01	PARAÍBA	0
26	1998-04-01	PARÁ	0
27	1998-05-01	ALAGOAS	0
28	1998-05-01	AMAPÁ	0
29	1998-05-01	MARANHÃO	0
30	1998-05-01	MATO GROSSO DO SUL	0
31	1998-05-01	RIO GRANDE DO SUL	0
32	1998-05-01	TOCANTINS	0
33	1998-06-01	ESPÍRITO SANTO	6
34	1998-06-01	RIO DE JANEIRO	3
35	1998-06-01	RIO GRANDE DO NORTE	1
36	1998-06-01	SÃO PAULO	451
37	1998-07-01	ESPÍRITO SANTO	37
38	1998-07-01	MARANHÃO	274
39	1998-07-01	MATO GROSSO	360
40	1998-07-01	MATO GROSSO DO SUL	3712
41	1998-07-01	PARAÍBA	0
42	1998-07-01	PARÁ	638
43	1998-07-01	RONDÔNIA	365
44	1998-07-01	SÃO PAULO	596
45	1998-08-01	ALAGOAS	1
46	1998-08-01	BAHIA	815
47	1998-08-01	DISTRITO FEDERAL	48
48	1998-08-01	ESPÍRITO SANTO	38
49	1998-08-01	MARANHÃO	1176
50	1998-08-01	MATO GROSSO	228
51	1998-08-01	MINAS GERAIS	875
52	1998-08-01	PIAUÍ	711
53	1998-08-01	RIO GRANDE DO SUL	9
54	1998-08-01	RORAIMA	0
55	1998-08-01	SERGIPE	0
56	1998-09-01	AMAPÁ	20
57	1998-09-01	DISTRITO FEDERAL	33
58	1998-09-01	PIAUÍ	1991
59	1998-09-01	RORAIMA	2
60	1998-10-01	AMAZONAS	83
61	1998-10-01	GOIÁS	1034
62	1998-10-01	MATO GROSSO	576
63	1998-10-01	PARAÍBA	179
64	1998-10-01	PARÁ	3665
65	1998-10-01	PIAUÍ	2586
66	1998-10-01	SERGIPE	0
67	1998-11-01	ALAGOAS	19
68	1998-11-01	AMAPÁ	131
69	1998-11-01	CEARÁ	575
70	1998-11-01	DISTRITO FEDERAL	0
71	1998-11-01	MARANHÃO	2237
72	1998-11-01	RIO DE JANEIRO	6
73	1998-11-01	RIO GRANDE DO SUL	28
74	1998-11-01	SÃO PAULO	488
75	1998-12-01	BAHIA	82
76	1998-12-01	MARANHÃO	1399
77	1998-12-01	MATO GROSSO	100
78	1998-12-01	PARAÍBA	51
79	1998-12-01	PERNAMBUCO	59
80	1998-12-01	RIO DE JANEIRO	1
81	1998-12-01	RONDÔNIA	33
82	1998-12-01	TOCANTINS	9
83	1999-01-01	ALAGOAS	58
84	1999-01-01	GOIÁS	14
85	1999-01-01	MATO GROSSO	239
86	1999-01-01	MINAS GERAIS	36
87	1999-01-01	PARÁ	87
88	1999-01-01	PERNAMBUCO	102
89	1999-01-01	RONDÔNIA	1
90	1999-01-01	SÃO PAULO	7
91	1999-01-01	TOCANTINS	36
92	1999-02-01	ACRE	0
93	1999-02-01	CEARÁ	16
94	1999-02-01	MATO GROSSO	69
95	1999-02-01	MATO GROSSO DO SUL	28
96	1999-02-01	PERNAMBUCO	13
97	1999-02-01	RONDÔNIA	1
98	1999-02-01	SANTA CATARINA	2
99	1999-02-01	TOCANTINS	1
100	1999-03-01	AMAPÁ	2

Rows: 1-100 | Columns: 3

Note

VerticaPy offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the VerticaPy environment.

Let’s select only one state to get a refined plot.

amazon = amazon[amazon["state"] == "ACRE"]

We can have a look at the time-series plot using the vDataFrame.plot():

amazon["number"].plot(ts = "date")

Now we can plot the ACF and PACF plots together:

from verticapy.machine_learning.model_selection import plot_acf_pacf

plot_acf_pacf(
    amazon,
    column = "number",
    ts = "date",
    p = 40,
)

	acf	pacf	confidence
0	1.0	1.0	0.1267795309147783
1	0.495679288734158	0.495679288734158	0.155151538705701
2	-0.00902491738328761	-0.341521587447086	0.1671963954865323
3	-0.139447406851902	0.0485607682545438	0.16777918642512535
4	-0.159024465260942	-0.139652084076008	0.17002131965100242
5	-0.162108279852924	-0.0644075843467855	0.17078344960052913
6	-0.163813499381328	-0.112455951499765	0.17236347056339854
7	-0.163181448364772	-0.103581755984784	0.17375997964460396
8	-0.160277838551237	-0.124071967093991	0.17559961328561075
9	-0.141430259231878	-0.111573132932377	0.1771584660187362
10	-0.0270293580716582	0.0157733583959032	0.1775683591823975
11	0.362489472613669	0.42479611272224	0.194292298973478
12	0.761692091124274	0.578722893043417	0.22192638408213544
13	0.376751291771019	-0.37120025821424	0.23270890340132178
14	-0.0338309868448387	0.173762484477621	0.23542537798173727
15	-0.143226889918242	-0.127816400911005	0.23713473329393076
16	-0.160050289828899	0.0321405741141465	0.2377406931331342
17	-0.162685637693094	-0.0519361958581586	0.2384713493358544
18	-0.164133347280837	-0.0101225425135962	0.23901772010284472
19	-0.164349761269233	-0.035838299909468	0.2396539247266604
20	-0.162199819237328	-0.0254986627714597	0.2402479321856056
21	-0.144610982270813	-0.0338203938556302	0.24088201802830564
22	-0.0263706130103819	0.0254502523194601	0.24148389480300914
23	0.396798954949423	0.371851235420011	0.25199741876215975
24	0.780928160448929	0.219777115027404	0.25597677160283533
25	0.36100402688506	-0.171946386761464	0.2586343799736745
26	-0.0573861008960089	0.0562306754073874	0.2594606669387884
27	-0.152954415331713	-0.0437277644885706	0.26020507213758454
28	-0.161224495595404	0.00993556098396334	0.2608278334856459
29	-0.163416632062352	-0.0206618413734289	0.2614779824787076
30	-0.165039473595857	-0.00485573569569075	0.26210443485045803
31	-0.165166417431331	-0.016216169971185	0.2627522222205266
32	-0.163084947808881	-0.00454974544129865	0.26338758326190487
33	-0.147449429124974	-0.0306277758861039	0.26409234538178394
34	-0.0505904416909773	-0.0416977243620344	0.26485873151048966
35	0.293491135087829	-0.0738273573287164	0.26589338723570816
36	0.647929656473852	0.086330180824858	0.2670760843095934
37	0.295423355893792	-0.0796782022332048	0.26818690615294377
38	-0.0607319645940286	0.0741251362590146	0.26924351205813934
39	-0.154665135387648	-0.0954142090531871	0.2705628403827728
40	-0.16325634494895	0.0508033230030744	0.2714254162225946

Rows: 1-41 | Columns: 4

See also

acf() : ACF plot from a vDataFrame.

pacf() : PACF plot from a vDataFrame.