scholarly journals Disentangling the stochastic behavior of complex time series

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Mehrnaz Anvari ◽  
M. Reza Rahimi Tabar ◽  
Joachim Peinke ◽  
Klaus Lehnertz
Keyword(s):  
Time Series ◽  
Stochastic Behavior ◽  
Complex Time

scholarly journals Scale-dependent intrinsic entropies of complex time series

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150204 ◽  
Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Spatial Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Complex Time ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Simulated Time ◽  
Simulated Time Series

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

Analysis of Time Series Based on a New Entropy Plane by Using Weighted Dispersion Pattern

2021 ◽  
Vol 31 (09) ◽  
pp. 2150128
Author(s):  
Guyue Qin ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Financial Markets ◽  
Stock Markets ◽  
Simulated Data ◽  
Tsallis Entropy ◽  
Dispersion Pattern ◽  
Complex Time ◽  
Plane Method ◽  
Stock Indexes ◽  
Analysis Of Time Series

Complexity is an important feature of complex time series. In this paper, we construct a weighted dispersion pattern and propose a new entropy plane using past Tsallis entropy and past Rényi entropy by using weighted dispersion pattern (PTEWD and PREWD, respectively), to quantify the complexity of time series. Through analyzing simulated data and actual data, we have verified the reliability of the entropy plane method. This entropy plane successfully distinguishes American and Chinese stock indexes, as well as developed and emergent stock markets. We introduce PTEWD and PREWD into multiscale settings, which could also well distinguish different stock markets. The results show that the new entropy plane could be used as an effective tool to distinguish financial markets.

Statistical Inference for Complex Time Series Data

2013 ◽  
Vol 10 (3) ◽  
pp. 2749-2823
Author(s):  
Rainer Dahlhaus ◽  
Oliver Linton ◽  
Wei-Biao Wu ◽  
Qiwei Yao
Keyword(s):  
Time Series ◽  
Statistical Inference ◽  
Time Series Data ◽  
Series Data ◽  
Complex Time

Clustering complex time-series databases by using periodic components

2017 ◽  
Vol 10 (2) ◽  
pp. 89-106 ◽  
Author(s):  
Francesco Giordano ◽  
Michele La Rocca ◽  
Maria Lucia Parrella
Keyword(s):  
Time Series ◽  
Complex Time ◽  
Periodic Components
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