The complexity-entropy causality plane, as a powerful tool for discriminating Gaussian from non-Gaussian process, has been recently introduced to describe the complexity among time series. We propose to use this method to distinguish the stage of climb-cruise-decline of aeroengine. Our empirical results demonstrate that this statistical physics approach is useful. Further, the return intervals based complexity-entropy causality plane is introduced to describe the complexity of aeroengine fuel flow time series. The results can infer that the cruise process has lowest complexity and the decline process has highest complexity
We propose novel metrics based on the Kolmogorov complexity for use in complex system behavior studi...
Complexity in time series is an intriguing feature of living dynamical systems, with potential use f...
International audienceThis paper aims at introducing the Lempel-Ziv permutation complexity vs. permu...
The search for patterns in time series is a very common task when dealing with complex systems. This...
Time series from chaotic and stochastic systems shape properties which can make it hard to distingui...
Chaotic systems share with stochastic processes several properties that make them almost undistingui...
One of the most useful tools for distinguishing between chaotic and stochastic time series is the so...
Complexity may be one of the most important measurements for analysing time series data; it covers o...
In order to effectively mine the structural features in time series and simplify the complexity of t...
Complexity measures are essential to understand complex systems and there are numerous definitions t...
The complexity-entropy causality plane has been recently introduced as a powerful tool for discrimin...
Information entropy is applied to the analysis of time series generated by dynamical systems. Comple...
Complexity measures are essential to understand complex systems and there are numerous definitions t...
We deal here with the issue of determinism versus randomness in time series. One wishes to identify ...
We construct a complexity measure from first principles, as an average over the ‘‘obstruction agains...
We propose novel metrics based on the Kolmogorov complexity for use in complex system behavior studi...
Complexity in time series is an intriguing feature of living dynamical systems, with potential use f...
International audienceThis paper aims at introducing the Lempel-Ziv permutation complexity vs. permu...
The search for patterns in time series is a very common task when dealing with complex systems. This...
Time series from chaotic and stochastic systems shape properties which can make it hard to distingui...
Chaotic systems share with stochastic processes several properties that make them almost undistingui...
One of the most useful tools for distinguishing between chaotic and stochastic time series is the so...
Complexity may be one of the most important measurements for analysing time series data; it covers o...
In order to effectively mine the structural features in time series and simplify the complexity of t...
Complexity measures are essential to understand complex systems and there are numerous definitions t...
The complexity-entropy causality plane has been recently introduced as a powerful tool for discrimin...
Information entropy is applied to the analysis of time series generated by dynamical systems. Comple...
Complexity measures are essential to understand complex systems and there are numerous definitions t...
We deal here with the issue of determinism versus randomness in time series. One wishes to identify ...
We construct a complexity measure from first principles, as an average over the ‘‘obstruction agains...
We propose novel metrics based on the Kolmogorov complexity for use in complex system behavior studi...
Complexity in time series is an intriguing feature of living dynamical systems, with potential use f...
International audienceThis paper aims at introducing the Lempel-Ziv permutation complexity vs. permu...