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In the framework of time series analysis with recurrence networks, weintroduce a self-adaptive method that determines the elusive recurrencethreshold and identifies metastable states in complex real-world time series.As initial step, we introduce a way to set the embedding parameters used toreconstruct the state space from the time series. We set them as the onesgiving the maximum Shannon entropy for the first simultaneous minima ofrecurrence rate and Shannon entropy. To identify metastable states, as well asthe transitions between them, we use a soft partitioning algorithm for modulefinding which is specifically developed for the case in which a system showsmetastability. We illustrate our method with two complex time series examples.Finally, we show the robustness of our method for identifying metastablestates. Our results suggest that our method is robust for identifyingmetastable states in complex time series, even when introducing considerablelevels of noise and missing data points.

Finding metastable states in real-world time series with recurrence networks