I'm trying find number of significant output using ACF graph, however results of statsmodels.tsa.acf() confidence intervals don't match with statsmodels.graphics.tsa.acf() graph.
Sample code:
import statsmodels.api as sm
from statsmodels.graphics.tsaplots import plot_acf,plot_pacf
acf,confidence_interval=sm.tsa.acf(df_data,nlags=df_data.shape[0]-1,alpha=0.05,fft=False)
plot_acf(df_data,lags=df_data.shape[0]-1)
print(confidence_interval)
Here is the plot,
However, confidence interval values returned from sm.tsa.acf() is way different comparing to the values in the graph.
Returned Values;
[[ 1. 1. ]
[-0.27174973 0.37268246]
[-0.3286431 0.31742828]
[ 0.0203798 0.66647139]
[-0.61221928 0.10569058]
[-0.61407253 0.14003004]
[-0.42569193 0.35873921]
[-0.58610165 0.19892257]
[-0.64565391 0.15895208]
[-0.34123344 0.49337893]
[-0.53223297 0.30525403]
[-0.56775509 0.2760946 ]
[-0.02246426 0.83178741]
[-0.55237867 0.37808097]
[-0.53964256 0.39420078]
[-0.19144858 0.74474359]
[-0.63752942 0.33201877]
[-0.66170085 0.31779123]
[-0.5026759 0.48927364]
[-0.63266561 0.35930273]
[-0.60042286 0.39933612]
[-0.50945575 0.49449365]
[-0.47942564 0.52454691]
[-0.48578234 0.51840072]
[-0.32312106 0.68117201]
[-0.40066389 0.61679615]
[-0.3917795 0.63043611]
[-0.35304025 0.67494402]
[-0.52974159 0.50865544]
[-0.57667548 0.46176601]
[-0.5657842 0.47397661]
[-0.61493365 0.42566845]
[-0.57909456 0.46507539]
[-0.54230719 0.50315461]
[-0.51974363 0.52587038]
[-0.53350424 0.5121135 ]
[-0.52597853 0.51968465]]
It seems like first value is matching the graph then, it becomes quite unrelated. I find similar question at Statsmodels PACF plot confidence interval does not match PACF function , yet there was no solution. I read the documents, search through similar questions but I cannot find a solution.
How can I get the confidence interval values that are reflected on the graph?