  A computationally quick and conceptually simple method to recover time delay
of the chaotic system from scalar time series is developed in this paper. We
show that the orbits in the incomplete two-dimensional reconstructed
phase-space will show local clustering phenomenon after the component
permutation procedure proposed in this work. We find that information captured
by the incomplete two-dimensional reconstructed phase-space, is related to the
time delay ${\tau _0}$ present in the system, and will be transferred to the
permutation component by the procedure of component permutation. We then
propose the segmented mean-variance (SMV) from the permutation component to
identify the time delay ${\tau _0}$ of the system. The proposed SMV shows clear
maximum when the embedding delay $\tau $ of the incomplete reconstruction
matches the time delay ${\tau _0}$ of the chaotic system. Numerical data
generated by a time-delay system based on the Mackey-Glass equation operating
in the chaotic regime are used to illustrate the effectiveness of the proposed
SMV. Experimental results show that the proposed SMV is robust to additive
observational noise and is able to recover the time delay of the chaotic system
even though the amount of data is relatively small and the feedback strength is
weak. Moreover, the time complexity of the proposed method is quite low.
