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Identification **Figure 16 shows the estimated autocorrelation (acf) in (a)**
and partial autocorrelation (pacf) in (b). The acf decreases quickly. The firstorder autocorrelation is high 0.**838 and it is of the same order of magnitude**
as for NK-landscapes with N = 100 and K **= 7 [33]. The acf is closed to the** two-standard-error bound from lag 40 and cuts this bound at lag 101 which is the correlation length. The fourth-order partial autocorrelation is close to the two-standard-error bound. The partial autocorrelation after **lag 4 tapers off** to zero. This suggests an AR(3) or AR**(4) model. The t-test on the estimation** coefficients of both model AR(3) and AR**(4) are significant, but p-values of** Box-Jenkins test show that residuals are not white noises. Thus, we tried to fit an ARMA(3, 1) model. The last autoregressive coefficient α3 **of the model**
is close to non-significant. In order to decide the significance of this **coefficient,** we extracted the sequence of the 980 first steps of the walk and estimated the model again. The t-test of α3 drops to 0.0738. So α3 **is non significant and not**
necessary. We thus end up with an ARMA(2, **1) model.**
Estimation The results of the ARMA(2, **1) model estimation is:**

$y_{t}=0.00281+1.5384y_{t-1}-0.5665y_{t-2}+\epsilon_{t}-0.7671\epsilon_{t-1}$  (20.4) (32.6) (13.7) (18.1)
3 **All the statistic results have been obtained with the R programming environment**
(see http://r-project.org)