verticapy.machine_learning.vertica.tsa.AR.deploySQL#

AR.deploySQL(ts: str | None = None, y: str | None = None, start: int | None = None, npredictions: int = 10, output_standard_errors: bool = False, output_index: bool = False) → str#

Returns the SQL code needed to deploy the model.

Parameters#

ts: str

TS (Time Series) :py:class`vDataColumn` used to order the data. The :py:class`vDataColumn` type must be date (date, datetime, timestamp…) or numerical.

y: str, optional

Response column.

start: int, optional

The behavior of the start parameter and its range of accepted values depends on whether you provide a timeseries-column (ts):

No provided timeseries-column:
start must be an integer greater or equal to 0, where zero indicates to start prediction at the end of the in-sample data. If start is a positive value, the function predicts the values between the end of the in-sample data and the start index, and then uses the predicted values as time series inputs for the subsequent npredictions.
timeseries-column provided:
start must be an integer greater or equal to 1 and identifies the index (row) of the timeseries-column at which to begin prediction. If the start index is greater than the number of rows, N, in the input data, the function predicts the values between N and start and uses the predicted values as time series inputs for the subsequent npredictions.

Default:

No provided timeseries-column:
prediction begins from the end of the in-sample data.
timeseries-column provided:
prediction begins from the end of the provided input data.

npredictions: int, optional

integer greater or equal to 1, the number of predicted timesteps.

output_standard_errors: bool, optional

boolean, whether to return estimates of the standard error of each prediction.

output_index: bool, optional

boolean, whether to return the index of each position.

Returns#

str: the SQL code needed to deploy the model.

Examples#

We import verticapy:

import verticapy as vp

For this example, we will use the airline passengers dataset.

import verticapy.datasets as vpd

data = vpd.load_airline_passengers()

	📅 date Date	123 passengers Integer
1	1949-01-01	112
2	1949-02-01	118
3	1949-03-01	132
4	1949-04-01	129
5	1949-05-01	121
6	1949-06-01	135
7	1949-07-01	148
8	1949-08-01	148
9	1949-09-01	136
10	1949-10-01	119
11	1949-11-01	104
12	1949-12-01	118
13	1950-01-01	115
14	1950-02-01	126
15	1950-03-01	141
16	1950-04-01	135
17	1950-05-01	125
18	1950-06-01	149
19	1950-07-01	170
20	1950-08-01	170
21	1950-09-01	158
22	1950-10-01	133
23	1950-11-01	114
24	1950-12-01	140
25	1951-01-01	145
26	1951-02-01	150
27	1951-03-01	178
28	1951-04-01	163
29	1951-05-01	172
30	1951-06-01	178
31	1951-07-01	199
32	1951-08-01	199
33	1951-09-01	184
34	1951-10-01	162
35	1951-11-01	146
36	1951-12-01	166
37	1952-01-01	171
38	1952-02-01	180
39	1952-03-01	193
40	1952-04-01	181
41	1952-05-01	183
42	1952-06-01	218
43	1952-07-01	230
44	1952-08-01	242
45	1952-09-01	209
46	1952-10-01	191
47	1952-11-01	172
48	1952-12-01	194
49	1953-01-01	196
50	1953-02-01	196
51	1953-03-01	236
52	1953-04-01	235
53	1953-05-01	229
54	1953-06-01	243
55	1953-07-01	264
56	1953-08-01	272
57	1953-09-01	237
58	1953-10-01	211
59	1953-11-01	180
60	1953-12-01	201
61	1954-01-01	204
62	1954-02-01	188
63	1954-03-01	235
64	1954-04-01	227
65	1954-05-01	234
66	1954-06-01	264
67	1954-07-01	302
68	1954-08-01	293
69	1954-09-01	259
70	1954-10-01	229
71	1954-11-01	203
72	1954-12-01	229
73	1955-01-01	242
74	1955-02-01	233
75	1955-03-01	267
76	1955-04-01	269
77	1955-05-01	270
78	1955-06-01	315
79	1955-07-01	364
80	1955-08-01	347
81	1955-09-01	312
82	1955-10-01	274
83	1955-11-01	237
84	1955-12-01	278
85	1956-01-01	284
86	1956-02-01	277
87	1956-03-01	317
88	1956-04-01	313
89	1956-05-01	318
90	1956-06-01	374
91	1956-07-01	413
92	1956-08-01	405
93	1956-09-01	355
94	1956-10-01	306
95	1956-11-01	271
96	1956-12-01	306
97	1957-01-01	315
98	1957-02-01	301
99	1957-03-01	356
100	1957-04-01	348

Rows: 1-100 | Columns: 2

First we import the model:

from verticapy.machine_learning.vertica.tsa import ARIMA

Then we can create the model:

model = ARIMA(order = (12, 1, 2))

We can now fit the model:

model.fit(data, "date", "passengers")

To get the SQL query which uses Vertica functions use below:

model.deploySQL()
Out[5]: 'PREDICT_ARIMA( USING PARAMETERS model_name = \'"public"."_verticapy_tmp_arima_v_demo_efe5d234e22d11eea3a80242ac120002_"\', add_mean = True, npredictions = 10 ) OVER ()'

Important

For this example, a specific model is utilized, and it may not correspond exactly to the model you are working with. To see a comprehensive example specific to your class of interest, please refer to that particular class.

Examples: ARIMA; ARMA; AR; MA;