  A novel heuristic approach is proposed here for time series data analysis,
dubbed Generalized weighted permutation entropy, which amalgamates and
generalizes beyond their original scope two well established data analysis
methods: Permutation entropy, and Weighted permutation entropy. The method
introduces a scaling parameter to discern the disorder and complexity of
ordinal patterns with small and large fluctuations. Using this scaling
parameter, the complexity-entropy causality plane is generalized to the
complexity-entropy-scale causality box. Simulations conducted on synthetic
series generated by stochastic, chaotic, and random processes, as well as real
world data, are shown to produce unique signatures in this three dimensional
representation.
