  There is no single universally accepted definition of "Complexity". There are
several perspectives on complexity and what constitutes complex behaviour or
complex systems, as opposed to regular, predictable behaviour and simple
systems. In this paper, we explore the following perspectives on complexity:
"effort-to-describe" (Shannon entropy $H$, Lempel-Ziv complexity $LZ$),
"effort-to-compress" ($ETC$ complexity) and "degree-of-order" (Subsymmetry or
$SubSym$). While Shannon entropy and $LZ$ are very popular and widely used,
$ETC$ is a recently proposed measure for time series. In this paper, we also
propose a novel normalized measure $SubSym$ based on the existing idea of
counting the number of subsymmetries or palindromes within a sequence. We
compare the performance of these complexity measures on the following tasks: a)
characterizing complexity of short binary sequences of lengths 4 to 16, b)
distinguishing periodic and chaotic time series from 1D logistic map and 2D
H\'{e}non map, and c) distinguishing between tonic and irregular spiking
patterns generated from the "Adaptive exponential integrate-and-fire" neuron
model. Our study reveals that each perspective has its own advantages and
uniqueness while also having an overlap with each other.
