P
Pengjian Shang
Researcher at Beijing Jiaotong University
Publications - 270
Citations - 4275
Pengjian Shang is an academic researcher from Beijing Jiaotong University. The author has contributed to research in topics: Entropy (information theory) & Multifractal system. The author has an hindex of 32, co-authored 250 publications receiving 3407 citations.
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Multiscale multifractal analysis of traffic signals to uncover richer structures.
TL;DR: This study focuses not only on the fact that traffic signals have multifractal properties, but also that such properties depend on the time scale in which the multifractality is computed, and finds periodicities of traffic signals are the main source of the crossovers.
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Comparison of transfer entropy methods for financial time series
Jiayi He,Pengjian Shang +1 more
TL;DR: In this paper, the authors analyzed the relationship between 9 stock indices from the U.S., Europe and China (from 1995 to 2015) by using transfer entropy, effective transfer entropy (ETE), Renyi transfer entropy(RTE) and effective Renyi Transfer Entropy (ERTE).
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Transfer entropy between multivariate time series
Xuegeng Mao,Pengjian Shang +1 more
TL;DR: This paper proposes the method of transfer entropy based on the theory of time-delay reconstruction of a phase space, which is a model-free approach to detect causalities in multivariate time series and utilizes it to real-world data, namely financial time series, in order to characterize the information flow among different stocks.
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Multiscale entropy analysis of traffic time series
TL;DR: The traditional MSE method and MSPE method are employed to investigate complexities of different traffic series, and it is obtained that the complexity of weekend traffic time series differs from that of the workday time series, which helps to classify the series when making predictions.
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A comparison study on stages of sleep: Quantifying multiscale complexity using higher moments on coarse-graining
TL;DR: Results show that the generalized MSE (including MSE σ 2 and MSEskew) can identify the differences in chaotic more easily with less fluctuation of entropy values in different time scales.