标题：商朋见
研究领域
应用统计： 经济金融数据统计分析、金融时间序列分析、分形时间序列分析、复杂数据统计特征提取等。
时间序列分析(Time series analysis)是一种动态数据处理的统计方法。该方法基于概率论和数理统计方法，研究随机数据序列所遵从的统计规律，以用于解决实际问题。它重点研究如何有效地收集、处理和分析实际数据，提取有效信息、建立统计模型，并进行统计推断和预测，为相关决策提供依据和参考，它具有很强的应用性。
科研项目
目前正在主持的部分科研项目:
1.国家自然科学基金项目：“非平稳时间序列的DCCA分析”
2.教育部博士点基金 
3.其它部门项目：“金融数据处理及其波动预报模型研究”
4.其它部门项目：“中国证劵市场各板块相依性新方法研究”
5.北京市项目：“北京市交通拥堵指数预测算法研究”
目前在研项目主要涉及以下三方面内容：
1. 经济金融与信息系统中随机复杂结构的建模与推断: 与经济金融和信息科学研究领域交叉，研究复杂经济金融环境中核心的随机复杂结构问题（如定价，金融数据处理与模拟，最优投资策略，金融波动的统计分析，金融风险度量等问题），以及交通数据的统计建模、理论和方法。
2.海量信息处理及知识挖掘的理论与方法: 随着计算机测量与控制系统和各种智能化仪器的广泛应用，大量的过程数据被采集并存储下来。但是这些包含过程运行状态信息的数据往往没有被有效地利用，以至出现了所谓的“数据很多，信息很少”的现象。我们以经济金融、信息科学等领域内关键和核心问题中重要的随机复杂结构为基础，建立随机复杂结构与数据的共性理论框架和方法体系，刻画和分析一些具有重要理论和应用价值的金融系统和信息系统。
3. 基于数据驱动的系统监控与诊断: 通过对金融系统和信息系统中数据的采集、预处理（滤波、校正等）和分析（特征提取、模式分类等），监督复杂系统的运行状态，检测系统信息，诊断、分析和预测各系统的动态趋势，从而达到减小波动风险、保障系统可靠运行的目标，使系统始终处于最佳运行状态。
近些年发表论文情况（部分）
[63] Ganli Liao, Pengjian Shang, Scaling and complexity-entropy analysis in discriminating traffic dynamics 20 (2012) 233-243.
[62] Xiaojun Zhao,Pengjian Shang, Jingjing Huang, Continuous detrended cross-correlation analysis on generalized Weierstrass function, Eur. Phys. J. B (2012) DOI: 10.1140/epjb/e2012-30244-6. 
[61] Jianan Xia, Pengjian Shang, Multiscale entropy analysis of financial time series, Fluct. Noise Lett. (2012) DOI: 10.1142/S0219477512500332. 
[60] Wenbin Shi, Pengjian Shang, Cross-sample entropy statistic as a measure of synchronism and cross-correlation of stock markets, Nonlinear Dyn (2012) DOI 10.1007/s11071-012-0680-z. 
[59] Jingjing Huang,Pengjian Shang, Xiaojun Zhao, Multifractal diffusion entropy analysis on stock volatility in financial markets, Physica A 391 (2012) 5739–5745. 
[58]Aijing Lin,Pengjian Shang, Xiaojun Zhao, The cross-correlations of stock markets based on DCCA and time-delay DCCA, Nonlinear Dyn 67(2012) 425–435. (SCI) 
[57] Chongfeng Xu,Pengjian Shang, Wang Jing, Multifractal Detrended Cross-Correlation Analysis of BVP model time series, Nonlinear Dyn 69:1-2 (2012) 263-273. (SCI) 
[56] Aijing Lin,Pengjian Shang，Application of empirical mode ecomposition combined with k-nearest neighbors approach in financial time series forecasting, fluctuation and Noise Letters 11:2,(2012) 1250018 (14 pages). (SCI) 
[55] Xiaojun Zhao, Pengjian Shang, Chuang Zhao, Jing Wang, Rui Tao, Minimizing the trend effect on detrended cross-correlation analysis with empirical mode decomposition, Chaos, Solitons & Fractals 45 (2012) 166–173. (SCI) 
[54] Xiaojun Zhao, Pengjian Shang, Multifractal detrended cross-correlation analysis of chinese stock markets based on time delay, Fractals 19 (2011) 329–338. (SCI) 
[53] Jing Wang, Pengjian Shang, A new traffic speed forecasting method based on bi-pattern recognition, Fluctuation and Noise Letters 10 (2011) 59–75. (SCI) 
[52] Xiaojun Zhao,Pengjian Shang, Aijing Lin, Gang Chen, Multifractal Fourier detrended cross-correlation analysis of traffic signals, Physica A 390 (2011) 3670–3678. (SCI) 
[51] Aijing Lin, Pengjian Shang, Minimizing periodic trends by applying laplace transform, Fractals 19 (2011) 203–211. (SCI) 
[50] Aijing Lin,Pengjian Shang, The orthogonal V-system detrended fluctuation analysis, Fluctuation and Noise Letters 10, (2011) 189–206. (SCI) 
[49] Jie Song, Pengjian Shang, Effect of linear and nonlinear filters on multifractal detrended cross-correlation analysis, Fractals 19: 4 (2011) 1-11. (SCI) 
[48] Jingjing Huang, Pengjian Shang, A New Bi-objective Optimization Model for the Traffic Guidance and Control Coordination, Journal of Computational Information Systems 7:5 (2011)1744-1752. (EI) 
[47] Jing Wang, Pengjian Shang and Xiaojun Zhao, A new traffic speed forecasting method based on bi-pattern recognition, Fluctuation and Noise Letters, 10(1), pp 1-17, 2011. (SCI) 
[46] 黄静静, 商朋见，王爱文, 二次半定规划的原始对偶预估校正内点算法, 北京交通大学学报35:6 (2011) 136-141. 
[45] Jianhai Yue, Xiaojun Zhao, and Pengjian Shang, Effect of Trends on Detrended Fluctuation Analysis of Precipitation Series, Mathematical Problems in Engineering, 2010卷, pp 1-15, 2010. (SCI) 
[44] Pengjian Shang and Keqiang Dong, Modeling cross-correlations of traffic flow, International Journal of Bifurcation and Chaos, 20(1), pp 1-6, 2010. (SCI) 
[43] Xiaojun Zhao, Pengjian Shang and Yulei Pang, Power law and stretched exponential effects of extreme events in chinese stock markets, Fluctuation and Noise Letters, 9(2), pp 203-217, 2010. (SCI) 
[42] Aijing Lin and Pengjian Shang, B-spline detrended fluctuation analysis for minimizing the effect of trends, Dynamics of Continuous Discrete and Impulsive Systems-Series B-Applica -tions & Algorithms, V17, pp 387-396, 2010. (SCI) 
[41] P. J. Shang, K. Dong and L. Zhao, Empirical mode decomposition and correlation properties of traffic fluctuation, Fluctuation and Noise Letters, 9(2), pp. 1–12, 2010. (SCI) 
[40] K. Dong and P. J. Shang, Do self-similar sets with positive Lebesgue measure contain an interval, Applied Mathematics Letters, 23, pp. 207-211, 2010. (SCI) 
[39] N. Xu,P. J. Shang and S. Kamae, Modeling traffic flow correlation using DFA and DCCA, Nonlinear Dynamics, i61(1-2), pp 207-216, 2010/7/7. (SCI) 
[38] K. Dong, P. J. Shang and H. Zhang, The multi-dependent Hurst exponent in traffic time series, Applied Mechanics and Materials, 20-23, pp. 346-351, 2010. (EI) 
[37] J. Yue, K. Dong and P. J. Shang, Time-dependent Hurst Exponent in Traffic Time Series, Sciencepaper online, pp. 1-5, 2010.2.9. 
[36] P. J. Shang, N. Xu and S. Kamae, Chaotic analysis of time series in the sediment transport phenomenon, Chaos Solitons and Fractals, 41, pp. 368-379, 2009. (SCI) 
[35] P. J. Shang, A. J. Lin and L. Liu, Chaotic SVD method for minimizing the effect of exponential trends, Physica A, 388, pp. 720-726, 2009. (SCI) 
[34] P. J. Shang and T. Li, Multifractal characteristics of palmprint and its extracted algorithm, Applied Mathematical Modelling, 33, pp. 4378-4387, 2009. (SCI) 
[33] N. Xu and P. J. Shang, Minimizing the effect of exponential trends in detrended fluctuation, Chaos Solitons and Fractals, 41, pp. 311-316, 2009. (SCI) 
[32] J. Yue, K. Dong and P. J. Shang, Phase Space Prediction of Traffic Time Series, Journal of Computational Information Systems, 5, pp. 1257-1266, 2009. (EI) 
[31] 许娜, 商朋见, 于建玲, 胡广生, 袁广才, 侦测股市时间序列相关性的三种方法, 数学的实践与认识, V.39, No.5, pp.13-18, 2009. 
[30] 许娜, 商朋见, 探测气温时间序列的复杂性特征分析方法,中国科技论文在线, V.2, No.13, pp.1350-1355, 2009. 
[29] P. J. Shang, Y. B. Lu and S. Kamae, Detecting long-range correlations of traffic time series with multifractal detrended fluctuation analysis, Chaos Solitons and Fractals, 36, pp. 82-90, 2008. (SCI) 
[28] J. Z. Guan, P. J. Shang, J. Liu and J. Yu, Dynamic analysis and forecast of traffic conditions of urban expressways, Proceedings of the 6th International Conference on Traffic and Transportation Studies Congress 2008: Traffic and Transportation Studies ICTTS 2008, 322, pp. 843-851, 2008. (EI) 
[27] 许娜, 商朋见, 胡广生, 气温变化时间序列的复杂性分析, 北京交通大学学报, V.32, No.3, pp.98-101, 2008. (EI) 
[26] 于建玲, 商朋见, 关积珍, 空间重构方法在短时交通流预测中的应用, 2008第四届中国智能交通年会论文集, pp.166-172, 2008. 
[25] P. J. Shang, M. Wang and S. Kamae, Fractal nature of highway traffic data, Computers and Mathematics with Applications, 54, pp. 107-116, 2007. (SCI) 
[24] P. J. Shang and J. S. Shen, Multi-fractal analysis of highway traffic data, Chinese Physics, 16, pp. 365-373, 2007. (SCI) 
[23] B. J. Zang and P. J. Shang, Multifractal analysis of the Yellow River flows, Chinese Physics, 16, pp. 565-569, 2007. (SCI) 
[22] X. Li and P. J. Shang, Multifractal classification of road traffic flows, Chaos Solitons and Fractals, 31, pp. 1089-1094, 2007. (SCI) 
[21] T. Li and P. J. Shang, A Multifractal Approach to Palmprint Recognition, Acta Physica Sinica, 56, pp. 4393-4400, 2007. (SCI) 
[20] 李彤, 商朋见, 多重分形在掌纹识别中的研究, 物理学报, V.56, No.8, pp.4393-4400, 2007. (SCI)[40] 袁平平, 于建玲, 商朋见, 股市时间序列的多重分形消除趋势分析, 北京交通大学学报, V.31, No.6, pp.69-72, 2007. 
[19] P. J. Shang, X. W. Li and S. Kamae, Nonlinear analysis of traffic time series at different temporal scales, Physics Letters A, 357, pp. 314-318, 2006. (SCI) 
[18] P. J. Shang, Y. B. Lu and S. Kamae, The application of H&ouml;lder exponent to traffic congestion warning, Physica A, 370, pp.769-776, 2006. (SCI) 
[17] 胡广生, 臧保将, 商朋见, 股市时间序列的非线性分析, 北京交通大学学报, V.30, No.6, pp.60-64, 2006. 
[16] 商朋见, 于建玲, 探测交通时间序列长相关性的多重分形消除趋势波动分析方法, 中国科技论文在线, V.2, No.1, pp.123-128, 2006. 
[15] 于建玲, 臧保将, 商朋见, 股市时间序列的多重分形分析, 北京交通大学学报, V.30, No.6, pp.69-72, 2006. 
[14] P. J. Shang, X. W. Li and S. Kamae, Chaotic analysis of traffic time series, Chaos Solitons and Fractals, 25, pp. 121-128, 2005. (SCI) 
[13] P. J. Shang and S. Kamae, Fractal nature of time series in the sediment transport phenomenon, Chaos Solitons and Fractals, 26, pp. 997-1007, 2005. (SCI) 
[12] 臧保将, 商朋见, 孙玉朋, 齐次自相似集的多重分形谱分析, 北京交通大学学报，V.29, No.3, pp.43-46, 2005. 
[11] P. J. Shang, X. W. Li and S. Kamae, Fractal properties of de Rham-type curve associated to random walk, Chaos Solitons and Fractals, 21, pp. 695-700, 2004. (SCI) 
[10] P. J. Shang and K. Widder, Power series and the open set condition of Hutchinson, Journal of Physics A, 37, pp. 1219-1224, 2004. (SCI) 
[9] 樊庆菊, 孙玉朋, 商朋见, 关于De Rham 曲线的分形性质, 北京交通大学学报, V.28, No.6, pp.47-49, 2004. 
[8] 孙玉朋, 樊庆菊, 商朋见, Hausdorff维数的乘积公式在RN上的推广及应用, 北方交通大学学报, V.28, No.3, pp.34-37, 2004. 
[7] 樊庆菊, 商朋见, 关于Hausdorff测度的一个不等式, 北方交通大学学报, V.27, No.3, pp.43-45, 2003. 
[6] P. J. Shang and K. Widder, Fractal dimension of quasi-periodic orbits, Applied Mathematics Letters, 14, pp. 969-973, 2001. (SCI) 
[5] 商朋见, 三类自仿集的维数, 北方交通大学学报, V.25, No.3, pp.7-12, 2001. 
[4] P. J. Shang and A. Zaks, Hausdorff measure and dimension of some fractals in RN, 
Nonlinear Analysis, 45, pp. 817-824, 2001. (SCI) 
[3] P. J. Shang, Is the Hausdorff dimension of a set and its image equal under binary coding 
map, Chaos Solitons and Fractals, 11, pp. 1093-1096, 2000. (SCI) 
[2] 商朋见, 赵玲玲, 无穷数列集的Hausdorff测度和Hausdorff维数, 北方交通大学学报, V.24, No.2, 
pp.9-13, 2000. 
[1] P. J. Shang, Effects of symmetry on the existence of eigenvector of the Schr&ouml;dinger 
operator, Journal of Systems Science and Mathematical Science, 18, pp. 104-108, 1998.