Origin of the usefulness of the natural-time representation of complex time series

被引：89
|
作者：
Abe, S [1 ]
Sarlis, NV
Skordas, ES
Tanaka, HK
Varotsos, PA
机构：
[1] Univ Tsukuba, Inst Phys, Tsukuba, Ibaraki 3058571, Japan
[2] Univ Athens, Dept Phys, Solid State Sect, Athens 15784, Greece
[3] Univ Athens, Dept Phys, Solid Earth Phys Inst, Athens 15784, Greece
[4] Tokai Univ, Earthquake Predict Res Ctr, Shizuoka 4248610, Japan
来源：
PHYSICAL REVIEW LETTERS | 2005年 / 94卷 / 17期
关键词：
D O I：
10.1103/PhysRevLett.94.170601
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
The concept of natural time turned out to be useful in revealing dynamical features behind complex time series including electrocardiograms, ionic current fluctuations of membrane channels, seismic electric signals, and seismic event correlation. However, the origin of this empirical usefulness is yet to be clarified. Here, it is shown that this time domain is in fact optimal for enhancing the signals in time-frequency space by employing the Wigner function and measuring its localization property.
引用
收藏
页数：4
相关论文
共 50 条
  • [21] Chaotic Time Series Prediction by Qubit Neural Network with Complex-Valued Representation
    Ueguchi, Taisei
    Matsui, Nobuyuki
    Isokawa, Teijiro
    2016 55TH ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS OF JAPAN (SICE), 2016, : 1353 - 1358
  • [22] A new time evolving Gaussian series representation of the imaginary time propagator
    Shao, Jiushu
    Pollak, Eli
    JOURNAL OF CHEMICAL PHYSICS, 2006, 125 (13):
  • [23] Discrete Representation Learning for Multivariate Time Series
    Ajirak, Marzieh
    Elbau, Immanuel
    Solomonov, Nili
    Grosenick, Logan
    32ND EUROPEAN SIGNAL PROCESSING CONFERENCE, EUSIPCO 2024, 2024, : 1132 - 1136
  • [24] Temporal representation learning for time series classification
    Hu, Yupeng
    Zhan, Peng
    Xu, Yang
    Zhao, Jia
    Li, Yujun
    Li, Xueqing
    NEURAL COMPUTING & APPLICATIONS, 2021, 33 (08): : 3169 - 3182
  • [25] Hierarchical Multiresolution Representation of Streaming Time Series
    Manojlovic, Igor
    Svenda, Goran
    Erdeljan, Aleksandar
    Gavric, Milan
    Capko, Darko
    BIG DATA RESEARCH, 2021, 26
  • [26] A BILINEAR REPRESENTATION OF SEASONAL TIME-SERIES
    IWUEZE, IS
    DATA ANALYSIS, LEARNING SYMBOLIC AND NUMERIC KNOWLEDGE, 1989, : 269 - 286
  • [27] Generalized Cointegrated Time Series: Representation and Estimation
    ZHANG Xibin and ZHANG Shiying School of Management
    Journal of Systems Science and Systems Engineering, 1998, (02) : 3 - 5
  • [28] Identifying the spectral representation of Hilbertian time series
    Horta, Eduardo
    Ziegelmann, Flavio
    STATISTICS & PROBABILITY LETTERS, 2016, 118 : 45 - 49
  • [29] Pattern Frequency Representation for Time Series Classification
    Milanov, Sergey
    Georgieva, Olga
    2016 IEEE 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS (IS), 2016, : 478 - 483
  • [30] Kernel sparse representation for time series classification
    Chen, Zhihua
    Zuo, Wangmeng
    Hu, Qinghua
    Lin, Liang
    INFORMATION SCIENCES, 2015, 292 : 15 - 26
12345