To clarify, A fuzzy number $p$ is called L-R fuzzy number if its membership function $\mu_{p}$ can be expressed through reference functions $L$ and $R$ in the form
$$ \mu_{p} (x) = \begin{cases} \mu_1(x)=L((\overline x - x)/\alpha) & \text{ for } x<\overline x,\\ \mu_2(x)=R((x - \overline x)/\beta) & \text{ for } x\geq \overline x. \end{cases} $$
A fuzzy time series $(p_t)_t = (<x_t, \alpha_t, \beta_t>)_t$ is a time-indexed sequence of $L-R$ fuzzy numbers.
My question is is there a way to detect anomalies for this type of time series?. I understand anomalies as in the context of time series, that is, values that are unexpectedly out of range or that deffer greatly from the expected values of the series.