PMID- 33261338
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20201202
IS  - 1089-7682 (Electronic)
IS  - 1054-1500 (Linking)
VI  - 30
IP  - 11
DP  - 2020 Nov
TI  - Characterizing dynamics of time series via Hill-index complexity measure.
PG  - 113139
LID - 10.1063/5.0010462 [doi]
AB  - The ordinal patterns of time series provide a variety of information about the 
      dynamic characteristics of the underlying process. With the ordinal symbolic 
      approach, recently, a permutation-based Hill's diversity index has been proposed 
      as a useful tool for complex system analysis. However, this method just measures 
      the complexity of the system from the perspective of uncertainty, while the 
      underlying structural information of the system dynamics can hardly be captured. 
      To overcome this deficiency, this paper introduces a Hill-index complexity 
      measure (HICM). The numerical applications suggest that the proposed HICM has 
      greater discriminating power than the conventional statistical complexity measure 
      (SCM). With parameter r, even between chaotic systems with extremely similar 
      dynamics, HICM can make a clear differentiation, which can barely be achieved by 
      SCM. Besides, HICM with appropriate r is relatively more robust against noise 
      than SCM. In the empirical application to financial markets, the approach adopted 
      in this paper could differentiate the stage of stock market development. Combined 
      with multidimensional scaling based on the dynamic time wrapping distance, the 
      method could reveal the potential similarities among the stock market dynamics 
      and give a refined classification of these world indices.
FAU - Peng, Kun
AU  - Peng K
AUID- ORCID: 0000000175947846
AD  - Department of Mathematics, Beijing Jiaotong University, Beijing 100044, People's 
      Republic of China.
FAU - Shang, Pengjian
AU  - Shang P
AUID- ORCID: 0000000207120879
AD  - Department of Mathematics, Beijing Jiaotong University, Beijing 100044, People's 
      Republic of China.
FAU - Yin, Yi
AU  - Yin Y
AD  - School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong 
      University, Beijing 100044, People's Republic of China.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Chaos
JT  - Chaos (Woodbury, N.Y.)
JID - 100971574
SB  - IM
EDAT- 2020/12/03 06:00
MHDA- 2020/12/03 06:01
CRDT- 2020/12/02 05:20
PHST- 2020/12/02 05:20 [entrez]
PHST- 2020/12/03 06:00 [pubmed]
PHST- 2020/12/03 06:01 [medline]
AID - 10.1063/5.0010462 [doi]
PST - ppublish
SO  - Chaos. 2020 Nov;30(11):113139. doi: 10.1063/5.0010462.