vastorbit.machine_learning.model_selection.hp_tuning.plot_acf_pacf¶

vastorbit.machine_learning.model_selection.hp_tuning.plot_acf_pacf(vdf: VastFrame, 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 (VastFrame) – Input VastFrame.
column (str) – Response column.
ts (str) – VastColumn used as timeline to order the data. It can be a numerical or date-like type (date, datetime, timestamp…) VastColumn.
by (list, optional) – VastColumns 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:

acf, pacf, confidence

Return type:

TableSample

Examples

We import vastorbit:

import vastorbit as vo

Hint

By assigning an alias to vastorbit, we mitigate the risk of code collisions with other libraries. This precaution is necessary because vastorbit 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 vastorbit are used as intended without interfering with functions from other libraries.

For this example, we will use the amazon dataset.

import vastorbit.datasets as vod

amazon = vod.load_amazon()

	📅 date Date	Abc state Varchar(32)	123 number Integer
1	2016-01-01	PARAÍBA	18
2	2016-02-01	PARAÍBA	4
3	2016-03-01	PARAÍBA	1
4	2016-04-01	PARAÍBA	1
5	2016-05-01	PARAÍBA	1
6	2016-06-01	PARAÍBA	4
7	2016-07-01	PARAÍBA	22
8	2016-08-01	PARAÍBA	50
9	2016-09-01	PARAÍBA	131
10	2016-10-01	PARAÍBA	304
11	2016-11-01	PARAÍBA	132
12	2016-12-01	PARAÍBA	8
13	2016-01-01	PARÁ	1322
14	2016-02-01	PARÁ	430
15	2016-03-01	PARÁ	81
16	2016-04-01	PARÁ	66
17	2016-05-01	PARÁ	154
18	2016-06-01	PARÁ	502
19	2016-07-01	PARÁ	1579
20	2016-08-01	PARÁ	4863
21	2016-09-01	PARÁ	3953
22	2016-10-01	PARÁ	5281
23	2016-11-01	PARÁ	7879
24	2016-12-01	PARÁ	3300
25	2016-01-01	PERNAMBUCO	24
26	2016-02-01	PERNAMBUCO	19
27	2016-03-01	PERNAMBUCO	4
28	2016-04-01	PERNAMBUCO	8
29	2016-05-01	PERNAMBUCO	6
30	2016-06-01	PERNAMBUCO	4
31	2016-07-01	PERNAMBUCO	17
32	2016-08-01	PERNAMBUCO	42
33	2016-09-01	PERNAMBUCO	171
34	2016-10-01	PERNAMBUCO	319
35	2016-11-01	PERNAMBUCO	191
36	2016-12-01	PERNAMBUCO	161
37	2016-01-01	PIAUÍ	94
38	2016-02-01	PIAUÍ	97
39	2016-03-01	PIAUÍ	53
40	2016-04-01	PIAUÍ	35
41	2016-05-01	PIAUÍ	60
42	2016-06-01	PIAUÍ	153
43	2016-07-01	PIAUÍ	754
44	2016-08-01	PIAUÍ	1647
45	2016-09-01	PIAUÍ	1394
46	2016-10-01	PIAUÍ	2598
47	2016-11-01	PIAUÍ	1126
48	2016-12-01	PIAUÍ	374
49	2016-01-01	RIO DE JANEIRO	9
50	2016-02-01	RIO DE JANEIRO	16
51	2016-03-01	RIO DE JANEIRO	16
52	2016-04-01	RIO DE JANEIRO	45
53	2016-05-01	RIO DE JANEIRO	30
54	2016-06-01	RIO DE JANEIRO	37
55	2016-07-01	RIO DE JANEIRO	131
56	2016-08-01	RIO DE JANEIRO	241
57	2016-09-01	RIO DE JANEIRO	195
58	2016-10-01	RIO DE JANEIRO	30
59	2016-11-01	RIO DE JANEIRO	19
60	2016-12-01	RIO DE JANEIRO	5
61	2016-01-01	RIO GRANDE DO NORTE	15
62	2016-02-01	RIO GRANDE DO NORTE	2
63	2016-03-01	RIO GRANDE DO NORTE	1
64	2016-04-01	RIO GRANDE DO NORTE	0
65	2016-05-01	RIO GRANDE DO NORTE	1
66	2016-06-01	RIO GRANDE DO NORTE	4
67	2016-07-01	RIO GRANDE DO NORTE	13
68	2016-08-01	RIO GRANDE DO NORTE	24
69	2016-09-01	RIO GRANDE DO NORTE	44
70	2016-10-01	RIO GRANDE DO NORTE	129
71	2016-11-01	RIO GRANDE DO NORTE	93
72	2016-12-01	RIO GRANDE DO NORTE	75
73	2016-01-01	RIO GRANDE DO SUL	68
74	2016-02-01	RIO GRANDE DO SUL	55
75	2016-03-01	RIO GRANDE DO SUL	30
76	2016-04-01	RIO GRANDE DO SUL	32
77	2016-05-01	RIO GRANDE DO SUL	37
78	2016-06-01	RIO GRANDE DO SUL	261
79	2016-07-01	RIO GRANDE DO SUL	865
80	2016-08-01	RIO GRANDE DO SUL	1111
81	2016-09-01	RIO GRANDE DO SUL	628
82	2016-10-01	RIO GRANDE DO SUL	93
83	2016-11-01	RIO GRANDE DO SUL	105
84	2016-12-01	RIO GRANDE DO SUL	79
85	2016-01-01	RONDÔNIA	93
86	2016-02-01	RONDÔNIA	88
87	2016-03-01	RONDÔNIA	25
88	2016-04-01	RONDÔNIA	59
89	2016-05-01	RONDÔNIA	44
90	2016-06-01	RONDÔNIA	170
91	2016-07-01	RONDÔNIA	969
92	2016-08-01	RONDÔNIA	3675
93	2016-09-01	RONDÔNIA	4208
94	2016-10-01	RONDÔNIA	1844
95	2016-11-01	RONDÔNIA	401
96	2016-12-01	RONDÔNIA	148
97	2016-01-01	RORAIMA	1754
98	2016-02-01	RORAIMA	171
99	2016-03-01	RORAIMA	1081
100	2016-04-01	RORAIMA	126

Rows: 1-100 | Columns: 3

Note

vastorbit 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 vastorbit 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 VastFrame.plot():

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

Now we can plot the ACF and PACF plots together:

from vastorbit.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	0.4956793	0.4956793	0.15515153986757108
2	-0.009024917	-0.34152162	0.1671963987248046
3	-0.1394474	0.048560776	0.16777918973880393
4	-0.15902446	-0.13965213	0.17002132416812135
5	-0.16210829	-0.06440762	0.17078345455817712
6	-0.1638135	-0.112456046	0.17236347752969797
7	-0.16318145	-0.10358186	0.17375998863807843
8	-0.16027784	-0.12407207	0.17559962464166412
9	-0.14143026	-0.11157317	0.17715847810361873
10	-0.027029358	0.015773509	0.17756837174087073
11	0.36248946	0.4247969	0.19429236850351983
12	0.7616921	0.5787253	0.22192665765986883
13	0.3767513	-0.3712005	0.23270917856856066
14	-0.033830985	0.17376284	0.2354256601429028
15	-0.14322689	-0.12781657	0.2371350177977126
16	-0.16005029	0.03214064	0.23774097849119158
17	-0.16268563	-0.0519363	0.23847163588598974
18	-0.16413334	-0.010122564047751402	0.23901800732330247
19	-0.16434976	-0.035838462	0.23965421333321657
20	-0.16219983	-0.025498755	0.24024822173657268
21	-0.14461099	-0.033821065	0.24088231146275282
22	-0.026370613	0.025450256	0.24148418886869322
23	0.39679897	0.37185577	0.2519979398680095
24	0.78092813	0.21977735	0.2559772942029684
25	0.36100402	-0.171947	0.2586349142576604
26	-0.0573861	0.05623083	0.25946120323013666
27	-0.15295441	-0.04372787	0.2602056100597141
28	-0.1612245	0.009935601	0.2608283727222244
29	-0.16341664	-0.020661898	0.2614785230996206
30	-0.16503948	-0.004855762	0.2621049767776744
31	-0.16516642	-0.016216233	0.2627527655543581
32	-0.16308495	-0.0045497674	0.2633881279176183
33	-0.14744943	-0.030628238	0.26409289321972557
34	-0.05059044	-0.04169793	0.26485928164119005
35	0.29349113	-0.07382781427147896	0.2658939426901845
36	0.64792967	0.08633088	0.26707664858197155
37	0.29542336	-0.079678796	0.2681874775795322
38	-0.060731966	0.0741258	0.2692440910587496
39	-0.15466514	-0.09541516	0.2705634323233808
40	-0.16325635	0.050803825	0.27142601287474954

Rows: 1-41 | Columns: 4