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Entropy-based recurrence diagnostics for tipping points in complex dynamical systems: Evidence from cryptocurrency markets
Prof. Giuseppe Orlando
University of Bari Aldo Moro, Bari, Italy
Abstract:
We introduce a recurrence–entropy framework for detecting tipping points in noisy, nonstationary time se- ries. The method combines phase–space embeddings, recurrence plots (RPs), and Shannon entropy of line- length distributions to identify transitions in complex systems. Using both Gaussian benchmarks and Bitcoin price/variance series across daily and intraday horizons, we show that entropy peaks coincide with structural transitions and volatility shifts. Variance–based entropy proves more sensitive than price–entropy, particularly around abrupt changes. Results are robust across embedding dimensions and delays, highlighting universality and resilience to microstructure noise. Although demonstrated on financial data, the approach is applicable to diverse physical systems.
Brief Biography of the Speaker:
To be announced soon