Abstract:This article describes a refinement of recurrence analysis to determine the delay in the causal influence between a driver and a target, in the presence of additional perturbations affecting the time series of the response observable. The methodology is based on the definition of a new type of recurrence plots, the Conditional Joint Recurrence plot. The potential of the proposed approach resides in the great flexibility of recurrence plots themselves, which allows extending the technique to more than three qua… Show more
“…While outside the scope of this work, a more precise identification of all interaction γs may be possible through the use of recent results on the dynamics of coupled systems, e.g. [65], [69]- [71].…”
Delays in air transport can be seen as the result of two independent contributions, respectively stemming from the local dynamics of each airport and from a global propagation process; yet, assessing the relative importance of these two aspects in the final behaviour of the system is a challenging task. We here propose the use of the score obtained in a classification task, performed over vectors representing the profiles of delays at each airport, as a way of assessing their identifiability. We show how Deep Learning models are able to recognise airports with high precision, thus suggesting that delays are defined more by the characteristics of each airport than by the global network effects. This identifiability is higher for large and highly connected airports, constant through years, but modulated by season and geographical location. We finally discuss some operational implications of this approach.
“…While outside the scope of this work, a more precise identification of all interaction γs may be possible through the use of recent results on the dynamics of coupled systems, e.g. [65], [69]- [71].…”
Delays in air transport can be seen as the result of two independent contributions, respectively stemming from the local dynamics of each airport and from a global propagation process; yet, assessing the relative importance of these two aspects in the final behaviour of the system is a challenging task. We here propose the use of the score obtained in a classification task, performed over vectors representing the profiles of delays at each airport, as a way of assessing their identifiability. We show how Deep Learning models are able to recognise airports with high precision, thus suggesting that delays are defined more by the characteristics of each airport than by the global network effects. This identifiability is higher for large and highly connected airports, constant through years, but modulated by season and geographical location. We finally discuss some operational implications of this approach.
“…RP based analysis can be used to infer coupling directions or even causal links between different systems [65,66]. Thus, the next step would be to test this approach for its potential on causality testing even for event data.…”
The analysis of event time series is in general challenging. Most time series analysis tools are limited for the analysis of this kind of data. Recurrence analysis, a powerful concept from nonlinear time series analysis, provides several opportunities to work with event data and even for the most challenging task of comparing event time series with continuous time series. Here, the basic concept is introduced, the challenges are discussed, and the future perspectives are summarized.
“…It is further possible that a correct evaluation of these propagation structures will require tailored, yet to be developed metrics, on the line of what proposed in [41]. It has further to be noted that the problem of functional network reconstruction from data is still an open one even from a theoretical point of view, and that researchers within statistical physics are still improving our understanding of the process [75][76][77][78]. The problem here considered is further strongly connected with other research topics in statistical physics, like the dynamics of coupled oscillators with and without delays [79][80][81][82][83].…”
Section: Conclusion: Can Functional Network Disrupt Delay Propagation?mentioning
The characterisation of delay propagation is one of the major topics of research in air transport management, due to its negative effects on the cost-efficiency, safety and environmental impact of this transportation mode. While most research works have naturally framed it as a transportation process, the successful application of network theory in neuroscience suggests a complementary approach, based on describing delay propagation as a form of information processing. This allows reconstructing propagation patterns from the dynamics of the individual elements, i.e. from the evolution observed at individual airports, without the need of additional a priori information. We here apply this framework to the analysis of delay propagation in the European airspace between 2015 and 2018, describe the evolution of the observed structure, and identify the role of individual airports in it. We further use this analysis to illustrate the limitations and challenges associated to this approach, and to sketch a roadmap of future research in this evolving topic.
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