Time Series Analysis Package
ACF Method
rc = TSA.ACF (y, nlag, <lags>, <df>, <mu>, <acov>, <acf>, <acfstd>, <acf2std>, <acfnorm>, <acfprob> <acflprob>);
The ACF function computes autocorrelation and autocovariance for a time series array.
Required Arguments
You must specify the following arguments, separated by a comma:
- y
specifies the times series array.
- nlag
specifies the number of the lag to use in the calculation.
Returned Values
The ACF function returns the following values:
- rc
-
returns one of the following scalar return codes:
rc Termination Reason 0 Success < 0 Computational failure
Optional Returned Values
You can also specify the following arguments, separated by commas, to request additional returned values. If you do not want the value to be returned, enter a space for it.
- lags
returns the numbers of the lags that were used in the calculation.
- df
returns the number of observations used to compute acov and acf.
- mu
returns the mean estimate.
- acov
returns an array of covariance estimates, with nlag + 1 entries.
- acf
returns an array of autocorrelation estimates, with nlag + 1 entries.
- acfstd
returns an array of standard errors, with nlag + 1 entries.
- acf2std
returns an array of twice standard errors, with nlag + 1 entries.
- acfnorm
returns an array of normalized autocorrelation, with nlag + 1 entries.
- acfprob
returns an array of autocorrelation probabilities, with nlag + 1 entries.
- acflprob
returns an array of autocorrelation log probabilities, with nlag + 1 entries.
Example
This example uses the TSMODEL procedure to compute the autocorrelation of lag 3 of the time series Air:
proc tsmodel data=mylib.air outscalar=mylib.outscalars
outarray=mylib.outarray;
id date interval=month;
var air;
outscalars mu;
outarrays acf acov lags df acfstd;
require tsa;
submit;
declare object TSA(tsa);
rc=TSA.ACF(air, 3, lags, df, mu, acov, acf, acfstd, , , , );
endsubmit;
run;