Compute Sample ACF and PACF
This example shows how to compute the sample autocorrelation function (ACF) and partial autocorrelation function (PACF) to qualitatively assess autocorrelation.
The time series is 57 consecutive days of overshorts from a gasoline tank in Colorado.
Step 1. Load the data.
Load the time series of overshorts.
load(fullfile(matlabroot,'examples','econ','Data_Overshort.mat')) Y = Data; N = length(Y); figure plot(Y) xlim([0,N]) title('Overshorts for 57 Consecutive Days')
The series appears to be stationary.
Step 2. Plot the sample ACF and PACF.
Plot the sample autocorrelation function (ACF) and partial autocorrelation function (PACF).
figure subplot(2,1,1) autocorr(Y) subplot(2,1,2) parcorr(Y)
The sample ACF and PACF exhibit significant autocorrelation. The sample ACF has significant autocorrelation at lag 1. The sample PACF has significant autocorrelation at lags 1, 3, and 4.
The distinct cutoff of the ACF combined with the more gradual decay of the PACF suggests an MA(1) model might be appropriate for this data.
Step 3. Store the sample ACF and PACF values.
Store the sample ACF and PACF values up to lag 15.
acf = autocorr(Y,15); pacf = parcorr(Y,15); [length(acf) length(pacf)]
ans = 16 16
The outputs acf and pacf are vectors storing the sample autocorrelation and partial autocorrelation at lags 0, 1,...,15 (a total of 16 lags).