  We show that the existing methods for computing the f(\alpha) spectrum from a
time series can be improved by using a new algorithmic scheme. The scheme
relies on the basic idea that the smooth convex profile of a typical f(\alpha)
spectrum can be fitted with an analytic function involving a set of four
independent parameters. While the standard existing schemes [16, 18] generally
compute only an incomplete f(\alpha) spectrum (usually the top portion), we
show that this can be overcome by an algorithmic approach which is automated to
compute the Dq and f(\alpha) spectrum from a time series for any embedding
dimension. The scheme is first tested with the logistic attractor with known
f(\alpha) curve and subsequently applied to higher dimensional cases. We also
show that the scheme can be effectively adapted for analysing practcal time
series involving noise, with examples from two widely different real world
systems. Moreover, some preliminary results indicating that the set of four
independant parameters may be used as diagnostic measures is also included.
