Special Issue "Ordinal and Pattern-Based Quantifiers for Complex Time Series Analysis"
Deadline for manuscript submissions: closed (22 December 2021) | Viewed by 8954
Interests: complex systems; permutation entropy; reservoir computing; nonlinear dynamical systems; time series analysis; information processing
Interests: time series analysis; nonlinear dynamics; complex systems; data analysis; permutation entropy; ordinal patterns; chaos; long-range correlations; fractality; multifractality
When observing the world, we are often confronted with the need to analyze time series of unknown origin. We look for tools and techniques that are free of restrictive parametric model assumptions and can account for the temporal ordering structure in the time series. The main objective is to try to shed some lights on the underlying mechanisms that govern the system’s dynamics. This information can then be used to model and predict the behavior of the system under study. Methods that rely on the notions of entropy and ordinal symbolic dynamics offer a competitive edge since they are robust with respect to noise, computationally efficient, flexible, and invariant with respect to nonlinear monotonic transformations of the data. They have been shown to be especially useful for characterization, discrimination and classification purposes. Furthermore, a large amount of data with outliers and artifacts can be quickly, automatically and successfully analyzed by using these ordinal tools. Examples of methods based on constructing an ordinal symbolic representation of the time series that can unveil the complex dynamical content of nonlinear time series are the multiscale permutation entropy and its variants, the ordinal autocorrelation functions, the decay of the number of forbidden/unobserved ordinal patterns as a function of the time series length, and the quantifiers derived from ordinal transition networks, to mention only a few.
This Special Issue aims at identifying ordinal and pattern-based quantifiers for complex time series analysis. Applications of interest include, but are not limited to, the analysis of nonlinear systems with multiple timescales, the discrimination of different types of temporal correlations, the distinction between chaotic and stochastic dynamics, the identification of temporal scales characteristic of the underlying temporal dynamics, time series classification, time series segmentation, and time series irreversibility.
Dr. Miguel C. Soriano
Dr. Luciano Zunino
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- ordinal patterns
- permutation entropy
- ordinal autocorrelation functions
- forbidden ordinal patterns
- unobserved ordinal patterns
- ordinal transition networks
- time series analysis
- nonlinear dynamical systems
- delayed feedback
- complex systems