Entropy and time asymmetry are two intertwined aspects of a system's dynamics, with the production of entropy marking a clear direction in the temporal dimension. In the last few years, metrics to quantify both properties in time series have been designed around the same concept, i.e., the use of ordinal patterns. In spite of this, the relationship between these two families of metrics is yet not well understood. In this contribution, we study this problem by constructing an entropy-time asymmetry plane and evaluating it on a large set of synthetic and real-world time series. We show how the two metrics can at times behave independently, the main reason being the presence of patterns with turning points; due to this, they yield complementary information about the underlying systems, and they have different discriminating performance. Entropy and temporal asymmetry are two cardinal facets of any dynamical system. Statistical physics has long been studying their mutual relationship from a theoretical point of view; yet, the numerical quantification of these two concepts in real-world systems has advanced along parallel but separate paths, with the potential interplay between them remaining largely unclear. In this contribution, we tackle this issue by reconstructing an entropy-time asymmetry plane, juxtaposing two metrics calculated on time series symbolized through the concept of ordinal patterns. We show how this plane is an adequate phase space to better comprehend and track situations in which entropy and time asymmetry behave in independent or complementary ways; and we evaluate it through both synthetic and real-world time series.

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