plot_acf_pacf¶
In [ ]:
plot_acf_pacf(vdf,
column: str,
ts: str,
by: list = [],
p=15,
**style_kwds,)
Draws ACF and PACF Charts.
Parameters¶
| Name | Type | Optional | Description |
|---|---|---|---|
vdf | vDataFrame | ❌ | Input vDataFrame. |
column | str | ❌ | Input vcolumn to test. |
ts | str | ❌ | vcolumn used as timeline. It will be to use to order the data. It can be a numerical or type date like (date, datetime, timestamp...) vcolumn. |
by | list | ✓ | vcolumns used in the partition. |
p | int/list | ✓ | Int: the maximum number of lag to consider during the computation or List: the lags to include in the computation. p must be positive or a list of positive integers. |
**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 [2]:
from verticapy import *
amazon = vDataFrame("amazon_clean")
amazon["number"].plot(ts = "date")
Out[2]:
In [3]:
from verticapy.learn.model_selection import plot_acf_pacf
plot_acf_pacf(amazon,
column = "number",
ts = "date",
p = 40)
Out[3]:
 | acf | pacf | confidence |
| 0 | 1.0 | 1.0 | 0.12677953091477834 |
| 1 | 0.681 | 0.680791943478559 | 0.17635811053763534 |
| 2 | 0.224 | -0.448651760602 | 0.19431587020757393 |
| 3 | -0.106 | -0.056810938511524 | 0.19499672195507936 |
| 4 | -0.321 | -0.214072572565421 | 0.19920783402950884 |
| 5 | -0.421 | -0.132275379180003 | 0.2010667082835581 |
| 6 | -0.444 | -0.209271515399161 | 0.2050497710828973 |
| 7 | -0.425 | -0.22086005226401 | 0.20938483891715812 |
| 8 | -0.322 | -0.115381512422639 | 0.21088997523164352 |
| 9 | -0.12 | -0.0303897676702676 | 0.21142090542967368 |
| 10 | 0.186 | 0.195940057811936 | 0.21490010161856163 |
| 11 | 0.558 | 0.421096854695467 | 0.22882273960076843 |
| 12 | 0.786 | 0.354085600891183 | 0.23839868796477467 |
| 13 | 0.582 | -0.277539454272164 | 0.24434402010546982 |
| 14 | 0.184 | -0.0466619873562256 | 0.24503815910113524 |
| 15 | -0.129 | -0.0252179360480865 | 0.24562891469251005 |
| 16 | -0.323 | -0.0208627356674878 | 0.24620949094162303 |
| 17 | -0.422 | -0.0738925038464602 | 0.2471459776695441 |
| 18 | -0.445 | -0.0277152168151683 | 0.24775839671186387 |
| 19 | -0.424 | -0.0570139111880927 | 0.24854931191055846 |
| 20 | -0.324 | -0.0359062473532943 | 0.249206893259297 |
| 21 | -0.115 | 0.0368926532844579 | 0.2498738171394671 |
| 22 | 0.215 | 0.183517089844233 | 0.2528182062297603 |
| 23 | 0.603 | 0.290406504204887 | 0.259254137753213 |
| 24 | 0.812 | 0.148748898561334 | 0.26137328696950884 |
| 25 | 0.574 | -0.252848551418073 | 0.26632779888317776 |
| 26 | 0.164 | -0.0207691117701225 | 0.2669813898197994 |
| 27 | -0.134 | 0.0353140856274143 | 0.26769474980282326 |
| 28 | -0.33 | -0.0921054257296545 | 0.2689033274675358 |
| 29 | -0.425 | 0.0154355178357686 | 0.2695589819239978 |
| 30 | -0.447 | -0.0390214624263903 | 0.27030664827764966 |
| 31 | -0.432 | -0.0361906130217691 | 0.27104490437804446 |
| 32 | -0.339 | -0.0476484001557326 | 0.27185384167555543 |
| 33 | -0.146 | -0.0190915628486309 | 0.2725378227507441 |
| 34 | 0.155 | -0.0448275804550625 | 0.27333953753511986 |
| 35 | 0.573 | 0.311750035943525 | 0.2806082427371645 |
| 36 | 0.804 | 0.0433932016985829 | 0.28142518911642866 |
| 37 | 0.579 | -0.115879309798946 | 0.2830246303006458 |
| 38 | 0.172 | -0.0134882291090413 | 0.28374005275607883 |
| 39 | -0.125 | 0.0304452233355777 | 0.2845111009329001 |
| 40 | -0.328 | -0.0850278455500817 | 0.2857139410476577 |
Rows: 1-41 | Columns: 4