  We present a new method for detecting superdiffusive behaviour and for
determining rates of superdiffusion in time series data. Our method applies
equally to stochastic and deterministic time series data (with no prior
knowledge required of the nature of the data) and relies on one realisation (ie
one sample path) of the process. Linear drift effects are automatically removed
without any preprocessing. We show numerical results for time series
constructed from i.i.d. $\alpha$-stable random variables and from deterministic
weakly chaotic maps. We compare our method with the standard method of
estimating the growth rate of the mean-square displacement as well as the
$p$-variation method, maximum likelihood, quantile matching and linear
regression of the empirical characteristic function.
