Computer Algebra Recipes

350 CHAPTER 8. NONLINEAR DIAGNOSTIC TOOLS
The randomly generated time series is plotted in Figure 8.18.
> plot([seq([n,x[n]],n=1..N)],labels=["n","x[n]"]);
1
0.8
0.6
x[n]
0.4
0.2
0
50 100 n 150 200
Figure 8.18: Time series for random data.
If we didn't know how the series was produced, we would probably still
conclude from the ¯gure that we are dealing with random noise. But one might
have arrived at the same conclusion in the previous recipe from the time series
plot. As in that recipe, let's plot xn+1 versus xn using a point style, thus
producing Figure 8.19.
> plot([seq([x[n],x[n+1]],n=1..N-1)],style=point,symbol=
circle,symbolsize=16,labels=["x[n]","x[n+1]"]);
1
0.8
0.6
x[n+1]
0.4
0.2
0
0.2 0.4 x[n] 0.8 1
Figure 8.19: xn+1 versus xn for time series.