Time-Delay Identification Using Multiscale Ordinal Quantifiers
Time-delayed interactions naturally appear in a multitude of real-world systems due to the finite propagation speed of physical quantities. Often, the time scales of the interactions are unknown to an external observer and need to be inferred from time series of observed data. We explore, in this wo...
Guardado en:
| Autores principales: | , |
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| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
2021
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/125453 https://www.mdpi.com/1099-4300/23/8/969 |
| Aporte de: |
| id |
I19-R120-10915-125453 |
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| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Física Time-delay Time series Symbolic analysis Ordinal patterns Permutation entropy Weighted permutation entropy Ordinal Temporal Asymmetry Autocorrelation function Linear models Nonlinear models |
| spellingShingle |
Física Time-delay Time series Symbolic analysis Ordinal patterns Permutation entropy Weighted permutation entropy Ordinal Temporal Asymmetry Autocorrelation function Linear models Nonlinear models Soriano, Miguel Zunino, Luciano José Time-Delay Identification Using Multiscale Ordinal Quantifiers |
| topic_facet |
Física Time-delay Time series Symbolic analysis Ordinal patterns Permutation entropy Weighted permutation entropy Ordinal Temporal Asymmetry Autocorrelation function Linear models Nonlinear models |
| description |
Time-delayed interactions naturally appear in a multitude of real-world systems due to the finite propagation speed of physical quantities. Often, the time scales of the interactions are unknown to an external observer and need to be inferred from time series of observed data. We explore, in this work, the properties of several ordinal-based quantifiers for the identification of time-delays from time series. To that end, we generate artificial time series of stochastic and deterministic time-delay models. We find that the presence of a nonlinearity in the generating model has consequences for the distribution of ordinal patterns and, consequently, on the delay-identification qualities of the quantifiers. Here, we put forward a novel ordinal-based quantifier that is particularly sensitive to nonlinearities in the generating model and compare it with previously-defined quantifiers. We conclude from our analysis on artificially generated data that the proper identification of the presence of a time-delay and its precise value from time series benefits from the complementary use of ordinal-based quantifiers and the standard autocorrelation function. We further validate these tools with a practical example on real-world data originating from the North Atlantic Oscillation weather phenomenon. |
| format |
Articulo Articulo |
| author |
Soriano, Miguel Zunino, Luciano José |
| author_facet |
Soriano, Miguel Zunino, Luciano José |
| author_sort |
Soriano, Miguel |
| title |
Time-Delay Identification Using Multiscale Ordinal Quantifiers |
| title_short |
Time-Delay Identification Using Multiscale Ordinal Quantifiers |
| title_full |
Time-Delay Identification Using Multiscale Ordinal Quantifiers |
| title_fullStr |
Time-Delay Identification Using Multiscale Ordinal Quantifiers |
| title_full_unstemmed |
Time-Delay Identification Using Multiscale Ordinal Quantifiers |
| title_sort |
time-delay identification using multiscale ordinal quantifiers |
| publishDate |
2021 |
| url |
http://sedici.unlp.edu.ar/handle/10915/125453 https://www.mdpi.com/1099-4300/23/8/969 |
| work_keys_str_mv |
AT sorianomiguel timedelayidentificationusingmultiscaleordinalquantifiers AT zuninolucianojose timedelayidentificationusingmultiscaleordinalquantifiers |
| bdutipo_str |
Repositorios |
| _version_ |
1764820451703390211 |