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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Detecting long-range correlations of traffic time series with multifractal detrended fluctuation analysis
TL;DR: Wang et al. as discussed by the authors used Multifractal detrended fluctuation analysis (MFDFA) to study the traffic speed fluctuations and demonstrated that the speed time series, observed on the Beijing Yuquanying highway over a period of about 40 months, has a crossover time scale s x, where the signal has different correlation exponents in time scales s ǫ> s x and s s x.
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Chaotic analysis of traffic time series
TL;DR: Non-linear time series modeling techniques were applied to analyze the traffic data collected from the Beijing Xizhimen and indicated that chaotic characteristics obviously exist in the traffic system.
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Multifractal cross-correlation analysis based on statistical moments
TL;DR: Wang et al. as mentioned in this paper introduced a new method, multifractal cross-correlation analysis based on statistical moments (MFSMXA), to investigate the long-term crosscorrelations and cross-multifractality between time series generated from complex system.
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Multifractal Fourier detrended cross-correlation analysis of traffic signals
TL;DR: In this article, a Fourier filtering method was introduced to eliminate the trend effects and systematically investigate the multifractal cross-correlation of simulated and real traffic signals, and the crossover locations were found approximately corresponding to the periods of underlying trend.
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Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting
TL;DR: The results indicate that the proposed EEMD–MKNN model has a higher forecast precision than EMD–KNN, KNN method and ARIMA, and has high predictive precision for short-term forecasting.