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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: str | list[str] | 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	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

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.