# Autocorrelation ACF & PACF functions explanation

there I am trying to use autocorrelation and partial autocorrelation to get the p and q values of my first AR and MA model. I have followed the Box–Jenkins method up until now. Making my time series stationary and removing trend and seasonality. I am now trying to interpret my acf and pacf plots to get my parameter, but I am finding hard to understand some of my plots. While some of my time series plots are very obvious some are not. Take for instance a time series A when I plot the acf and pcf they both look the exact same to me and they seem as if they are both "cutting off"

ACF

PACF

As you can see they both look equal both cutting off after the first significant lag. Excuse me if I am looking at this all wrong but I would appreciate any help here guiding me in the right direction. Thanks

Update Posting the data as requested. It is in regards to bitcoin closing price over the last few weeks. This data was then differentiated by one also tried standardizing the data using score (This was advised to me, will this have a negative impact?), The following is original data

2018-02-26    10366.70
2018-02-27    10725.60
2018-02-28    10397.90
2018-03-01    10951.00
2018-03-02    11086.40
2018-03-03    11489.70
2018-03-04    11512.60
2018-03-05    11573.30
2018-03-06    10779.90
2018-03-07     9965.57
2018-03-08     9395.01
2018-03-09     9337.55
2018-03-10     8866.00
2018-03-11     9578.63
2018-03-12     9205.12
2018-03-13     9194.85
2018-03-14     8269.81
2018-03-15     8300.86
2018-03-16     8338.35
2018-03-17     7916.88
2018-03-18     8223.68
2018-03-19     8630.65
2018-03-20     8913.47
2018-03-21     8929.28
2018-03-22     8728.47
2018-03-23     8879.62
2018-03-24     8668.12
2018-03-25     8495.78
2018-03-26     8209.40
2018-03-27     7833.04
2018-03-28     7954.48
2018-03-29     7165.70
2018-03-30     6890.52
2018-03-31     6973.53
2018-04-01     6844.23
2018-04-02     7083.80
2018-04-03     7456.11
2018-04-04     6853.84
2018-04-05     6811.47
2018-04-06     6636.32
2018-04-07     6911.09
2018-04-08     7023.52
2018-04-09     6770.73
2018-04-10     6834.76
2018-04-11     6968.32
2018-04-12     7889.25
2018-04-13     7895.96
2018-04-14     7986.24
2018-04-15     8329.11

• Your software is plotting the zero order coefficients, which is both useless and confusing. You need to ignore those. Then you'll see that neither plot "cuts off", there are just no significant spikes, which is consistent with $p=q=0$ (white noise). – Chris Haug Apr 16 '18 at 14:09
• @ChrisHaug Thanks for your reply. Makes sense what you are saying. Would "smoothing" of the data help in this case – Alexander Kirwan Apr 16 '18 at 15:26
• What do you mean by "smoothing" and what are you trying to "help" by doing so? – Chris Haug Apr 16 '18 at 17:05