Quantifying Deviations from Gaussianity with Application to Flight Delay Distributions
We propose a novel approach for quantifying deviations from Gaussianity by leveraging the Jensen-Shannon distance. Using stable distributions as a flexible framework, we analyze the effects of skewness and heavy tails in synthetic sequences. We employ phase-randomized surrogates as Gaussian referenc...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/401584 |
| Acceso en línea: | http://hdl.handle.net/10261/401584 http://arxiv.org/abs/2503.05834v1 |
| Access Level: | acceso abierto |
| Palabra clave: | Jensen–Shannon divergence Air traffic management Flight delays Non-Gaussian distributions Ordinal patterns Stable distributions |
| Sumario: | We propose a novel approach for quantifying deviations from Gaussianity by leveraging the Jensen-Shannon distance. Using stable distributions as a flexible framework, we analyze the effects of skewness and heavy tails in synthetic sequences. We employ phase-randomized surrogates as Gaussian references to systematically evaluate the statistical distance between this reference and stable distributions. Our methodology is validated using real flight delay datasets from major airports in Europe and the United States, revealing significant deviations from Gaussianity, particularly at high-traffic airports. These results highlight systematic air traffic management strategy differences between the two geographic regions. |
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