More than ten years ago Bandt and Pompe introduced a new measure to quantify
complexity in measured time series. During these ten years, this measure has been modified …

Ordinal symbolic analysis opens an interesting and powerful perspective on time-series
analysis. Here, we review this relatively new approach and highlight its relation to symbolic …

In this paper we introduce a multiscale symbolic information-theory approach for
discriminating nonlinear deterministic and stochastic dynamics from time series associated …

Order patterns apply well to many fields, because of minimal stationarity assumptions. Here,
we fix the methodology of patterns of length 3 by introducing an orthogonal system of four …

It is widely known that commodity markets are not totally efficient. Long-range dependence
is present, and thus the celebrated Brownian motion of prices can be considered only as a …

Local patterns play an important role in statistical physics as well as in image processing.
Two-dimensional ordinal patterns were studied by Ribeiro et al. who determined …

Order patterns and permutation entropy have become useful tools for studying biomedical,
geophysical or climate time series. Here we study day-to-day market data, and Brownian …

We introduce a representation space to contrast chaotic with stochastic dynamics. Following
the complex network representation of a time series through ordinal pattern transitions, we …

A list of new tests for serial dependence based on ordinal patterns is provided. These new
methods rely exclusively on the order structure of the data sets. Hence, the novel tests are …

When using ordinal patterns, which describe the ordinal structure within a data vector, the
problem of ties appears permanently. So far, model classes were used which do not allow …