Classifying of financial time series based on multiscale entropy and multiscale time irreversibility

2014 ◽  
Vol 400 ◽  
pp. 151-158 ◽  
Author(s):  
Jianan Xia ◽  
Pengjian Shang ◽  
Jing Wang ◽  
Wenbin Shi
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Multiscale Entropy ◽  
Financial Time ◽  
Time Irreversibility ◽  
Multiscale Time Irreversibility

MULTISCALE ENTROPY ANALYSIS OF FINANCIAL TIME SERIES

2012 ◽  
Vol 11 (04) ◽  
pp. 1250033 ◽  
Author(s):  
JIANAN XIA ◽  
PENGJIAN SHANG
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Sample Entropy ◽  
Scale Factor ◽  
Multiscale Entropy ◽  
Entropy Analysis ◽  
Financial Time

The paper mainly applies the multiscale entropy (MSE) to analyze the financial time series. The MSE is used to examine the complexity of a quantified system. Based on MSE, we propose multiscale cross-sample entropy (MSCE) to analyze the complexity and correlation of two time series. By comparing with the results, we find that both results present remarkable scaling characterization and the value of each log return of financial time series decreases with a increasing scale factor. From the results of MSE, we also find that the entropy of the Europe markets is lower than that of the Asia, but higher than that of the Americas. It means the MSE can distinguish different areas markets. The results of MSCE show that financial plate have high synchrony with the plate of Electron, IT and Realty. The MSCE can distinguish the highly synchronous plates.

scholarly journals Measuring the Topological Time Irreversibility of Time Series With the Degree-Vector-Based Visibility Graph Method

2021 ◽  
Vol 9 ◽  
Author(s):  
Ryutaro Mori ◽  
Ruiyun Liu ◽  
Yu Chen
Keyword(s):  
Time Series ◽  
Random Walk ◽  
Time Reversal ◽  
Financial Time Series ◽  
Visibility Graph ◽  
Topological Properties ◽  
Network Nodes ◽  
Financial Time ◽  
Time Irreversibility ◽  
Synthetic Time Series

Time irreversibility of a time series, which can be defined as the variance of properties under the time-reversal transformation, is a cardinal property of non-equilibrium systems and is associated with predictability in the study of financial time series. Recent pieces of literature have proposed the visibility-graph-based approaches that specifically refer to topological properties of the network mapped from a time series, with which one can quantify different degrees of time irreversibility within the sets of statistically time-asymmetric series. However, all these studies have inadequacies in capturing the time irreversibility of some important classes of time series. Here, we extend the visibility-graph-based method by introducing a degree vector associated with network nodes to represent the characteristic patterns of the index motion. The newly proposed method is parameter-free and temporally local. The validation to canonical synthetic time series, in the aspect of time (ir)reversibility, illustrates that our method can differentiate a non-Markovian additive random walk from an unbiased Markovian walk, as well as a GARCH time series from an unbiased multiplicative random walk. We further apply the method to the real-world financial time series and find that the price motions occasionally equip much higher time irreversibility than the calibrated GARCH model does.

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