MATLAB POLYSPACE RELEASE NOTES Manual do Utilizador descarregar pdf (Página 177)

Preprocessing Data

Noisy data shows random variations about expected values. You may want

to smooth the data to reveal its main features before building a model. Two

basic assumptions u nderlie smoothing:

• The relationship between the predictor (time) and the res po nse (traffic

volume) is smooth.

• The smoothing algorithm results in v a lues that are better estim ates of

expected values because the noise has been reduced.

Apply a simple moving average smoother to the data using the MATLAB

convn function:

span = 3; % Size of the averaging window

window = ones(span,1)/span;

smoothed_c3m = convn(c3m,window,'s ame' );

h = plot(smoothed_c3m, 'ro-');

legend('Data','Smoothed Data')

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MATLAB POLYSPACE RELEASE NOTES Manual do Utilizador Página 177