Review and Prospect of System Management Prediction Technology Based on TEI@I Methodology

Abstract

Abstract: With the changes in the world economy and industrial structure, the uncertainty and risk of the market economy are increasing. The predictive technology with early warning has become a research hot spot of many scholars. But with the advent of the big data, the behavior in complex systems becomes difficult to control and predict. In this paper, we review the development and application of prediction technology from the perspective of system management methodology with the help of text mining and bibliometric. Firstly, the development of system management forecasting methods is systematically summarized and evaluated based on the extensive investigation of the main forecasting technical literature. Besides, the principle and application of TEI@I methodology and its derived prediction methods in system management are summarized. Secondly, the trend of relevant literature in recent 20 years and the popular forecasting technologies are analyzed by bibliometric method. Finally, we summarize the whole paper and forecast the development of system management prediction technology under the background of big data in the future.

Key words: system management methodology, prediction technology, TEI@I methodology, bibliometric, big data

Chai Jian, Kou Honghong. Review and Prospect of System Management Prediction Technology Based on TEI@I Methodology[J]. Management Review, 2020, 32(7): 280-292.

References

[1] 钱学森. 一个科学新领域——开放的复杂巨系统及其方法论[J]. 上海理工大学学报, 2011,33(6):26-32
[2] 杰弗里·A.艾斯特凡著,张新国译. 基于模型的系统工程(MBSE)方法论综述[M]. 北京:机械工业出版社, 2014
[3] 吕永波. 系统工程[M]. 北京:清华大学出版社, 2005
[4] 许国志,顾基发. 系统工程的回顾与展望[J]. 系统工程理论与实践, 1990,10(6):1-15
[5] 李怀祖. 管理研究方法论[M]. 西安:西安交通大学出版社, 2004
[6] Wang S. Y., Yu L. A., Lai K. K. Crude Oil Price Forecasting with TEI@I Methodology[J]. Journal of Systems Science and Complexity, 2005,18(2):145-166
[7] 吴清烈,蒋尚华. 预测与决策分析[M]. 南京:东南大学出版社, 2004
[8] 徐国祥. 统计预测与决策[M]. 上海:上海财经大学出版社, 2008
[9] Gooijer J. G. D., Hyndman R. J. 25 Years of Time Series Forecasting[J]. Internationa Journal of Forecasting, 2006,22(3):443-473
[10] Armstrong J. S., Fildes R. Making Progress in Forecasting[J]. International Journal of Forecasting, 2006,22(3):433-441
[11] 汪寿阳,余乐安,房勇,等. 国际油价波动分析与预测[M]. 长沙:湖南大学出版社, 2008
[12] 余乐安,汪寿阳,黎建强. 外汇汇率与国际原油价格波动预测——汇率与国际方法论[M]. 长沙:湖南大学出版社, 2006
[13] Lawrence M., Goodwin P., O'Connor M., et al. Judgmental Forecasting:A Review of Progress Oer the Last 25 Years[J]. International Journal of Forecasting, 2006,22(3):493-518
[14] Sanders N. R., Manrodt K. B. The Efficacy of Using Judgmental Versus Quantitative Forecasting Methods in Practice[J]. Omega, 2003,31(6):511-522
[15] 杨春,李怀祖. 一个证据推理模型及其在专家意见综合中的应用[J]. 系统工程理论与实践, 2001,21(4):43-48
[16] 杨兴雨,刘悦,杨晓光,等. 带交易费用的集成专家意见在线投资组合策略[J]. 系统工程理论与实践, 2018,38(8):1946-1959
[17] Jiang R., Kleer R., Piller F. T. Predicting the Future of Additive Manufacturing:A Delphi Study on Economic and Societal Implications of 3D Printing for 2030[J]. Technological Forecasting and Social Change, 2017,117(4):84-97
[18] Wu K. J., Liao C. J., Tseng M. L., et al. Toward Sustainability:Using Big Data to Explore the Decisive Attributes of Supply Chain Risks and Uncertainties[J]. Journal of Cleaner Production, 2017,142(2):663-676
[19] 崔志明,万劲波,孟晓华,等. 技术预见"市场德尔菲法"的特点及实施程序探讨[J]. 科学学与科学技术管理, 2004,25(12):13-17
[20] Kahn H., Wiener A. J. The Year 2000[M]. London:Macmillan, 1967
[21] 柴建,卢全莹,邢丽敏,等. 中国天然气产业的发展过快了吗?[J]. 管理评论, 2017,29(8):23-32
[22] Lee S., Cho C., Hong E., et al. Forecasting Mobile Broadband Traffic:Application of Scenario Analysis and Delphi Method[J]. Expert Systems with Applications, 2016,44(2):126-137
[23] Myers R. H. Classical and Modern Regression with Applications[M]. 北京:高等教育出版社, 2008
[24] 谢宇. 回归分析[M]. 北京:社会科学文献出版社, 2013
[25] Harrington P.著,李锐译. 机器学习实战[M]. 北京:人民邮电出版社,2013
[26] Cleveland W. S., Devlin S. J. Locally Weighted Regression:An Approach to Regression Analysisby LocalFitting[J]. Journal of the American Statistical Association, 1988,83(403):596-610
[27] Hoerl A. E., Kennard R. W. Ridge Regression:Biased Estimation for Non Orthogonal Problems[J]. Technometrics, 1970,12(1):55-67
[28] Motulsky H. J., Ransnas L. A. Fitting Curves to Data Using Nonlinear Regression:A Practical and Nonmathematical Review[J]. The FASEB Journal, 1987,1(5):365-374
[29] 刘长生,郭小东,简玉峰. 能源消费对中国经济增长的影响研究——基于线性与非线性回归方法的比较分析[J]. 产业经济研究, 2009,(1):1-9
[30] 郦金梁,何诚颖,陈伟,等. 特质风险与公司投资行为选择——基于变量间非线性关系的视角[J]. 管理世界, 2018,34(3):68-77
[31] 谷克鉴,陈福中. 净出口的非线性增长贡献——基于1995非线性增长年中国省级面板数据的实证考察[J]. 经济研究, 2016,51(11):13-27
[32] Newbold P. The Competition to End All Competitions[J]. Journal of Forecasting, 1983,2(3):276-279
[33] 肖皓,杨佳衡,乔晗. 需求侧全球碳排放强度的度量及分解[J]. 系统工程理论与实践, 2015,35(7):1646-1656
[34] 李晖. 基于LMDI分解技术的中国分行业劳动报酬增长因素分析[J]. 管理评论, 2018,30(5):148-157
[35] 韩豫峰,汪雄剑,周国富,等. 中国股票市场是否存在趋势?[J]. 金融研究, 2014,(3):152-163
[36] Barrow D. K. Forecasting Intraday Call Arrivals Using the Seasonal Moving Average Method[J]. Journal of Business Research, 2016,69(12):6088-6096
[37] Khamooshi H., Abdi A. Project Duration Forecasting Using Earned Duration Management with Exponential Smoothing Techniques[J]. Journal of Management in Engineering, 2017,33(1):1-10
[38] Maia A. L. S., de Carvalho F. A. T. Holt's Exponential Smoothing and Neural Network Models for Forecasting Interval-valued Time Series[J]. International Journal of Forecasting, 2011,27(3):740-759
[39] Meng M., Jing K., Mander S. Scenario Analysis of CO2 Emissions from China's Electric Power Industry[J]. Journal of Cleaner Production, 2017,142(1):3101-3108
[40] Yu S., Zheng S., Li X., et al. China Can Peak Its Energy-related Carbon Emissions Before 2025:Evidence from Industry Restructuring[J]. Energy Economics, 2018,73(6):91-107
[41] Chatfield C. The Analysis of Time Series[M]. London:Chapman and Hall, 1996
[42] Bunn D., Wright G. Interaction of Judgemental and Statistical Forecasting Methods:Issues & Analysis[J]. Management Science, 1991,37(5):501-518
[43] Armstrong J. S. Extrapolation for Time-Series and Cross-Sectional Data[J]. Principles of Forecasting, 2001,30(3):217-243
[44] De Gooijer J. G., Hyndman R. J. 25 Years of Time Series Forecasting[J]. International Journal of Forecasting, 2006,22(3):443-473
[45] 詹姆斯·D.汉密尔顿著,夏晓华译. 时间序列列分析[M]. 北京:中国人民大学出版社, 2015
[46] Valipour M., Banihabib M. E., Behbahani S. M. R. Comparison of the ARMA and the Autoregressive Artificial Neural Network Models in Forecasting the Monthly Inflow of Dez Dam Reservoir[J]. Journal of Hydrology, 2013,476(1):433-441
[47] Wang Q., Li S., Li R., et al. Forecasting US Shale Gas Monthly Production Using a Hybrid ARIMA and Metabolic Nonlinear Grey Model[J]. Energy, 2018,160(8):378-387
[48] 李蕾,李超,丁雪辰,等. 我国执业(助理)医师需求集成预测——基于GM、ARIMA和VAR模型的实证研究[J]. 管理评论, 2018,30(3):171-178
[49] Tong H., Lim K. S. Threshold Autoregression Limit Cycles and Cyclical Data[J]. Journal of the Royal Statistical Society, 1980,42(3):245-292
[50] 周弘,张成思,何启志. 中国居民资产配置效率的门限效应研究:金融约束视角[J]. 金融研究, 2018,(10):59-75
[51] 刘金全,郑挺国. 我国经济周期阶段性划分与经济增长走势分析[J]. 中国工业经济, 2008,(1):32-39
[52] 靳晓婷,张晓峒,栾惠德. 汇改后人民币汇率波动的非线性特征研究——基于门限自回归TAR模型[J]. 财经研究, 2008,34(9):48-57
[53] Sims C. A. Macroeconomics and Reality[J]. Econometrica, 1980,48(1):1-48
[54] Hansen L. P., Sargent T. J. Two Difficulties in Interpreting Vector Autoregressions[M]. Boulder:Westview Press, 1991
[55] Leamer E. E. Vector Autoregression for Causal Inference?[J]. Carnegie-rochester Conference Series on Public, 1985,22(1):255-304
[56] Sims C. A. Are Forecasting Models Usable for Policy Analysis?[J]. Quarterly Review, 1986,10(2):2-16
[57] Amisano G., Giannini C. Topics in Structural VAR Econometrics[M]. Berlin:Springer-Verlag, 1997
[58] Schorfheide F., Song D. Real-Time Forecasting with a Mixed-Frequency VAR[J]. Journal of Business & Economic Statistics, 2013,33(3):366-380
[59] 伍雪冬. 非线性时间序列在线预测建模与仿真[M]. 北京:国防工业出版社, 2015
[60] 陈桦,赵晓,齐慧. 基于决策支持系统的预测模型研究[J]. 微电子学与计算机, 2004,21(12):166-167
[61] Fildes R., Goodwin P. Forecasting Support Systems:What We Know, What We Need to Know[J]. International Journal of Forecasting, 2013,29(2):290-294
[62] Moon M. A., Mentzer J. T., Smith C. D. Conducting a Sales Forecasting Audit[J]. International Journal of Forecasting, 2003,19(1):5-25
[63] Fildes R., Goodwin P., Lawrence M. The Design Features of Forecasting Support Systems and Their Effectiveness[J]. Decision Support Systems, 2006,42(1):351-361
[64] Van Bruggen G. H., Spann M., Lilien G. L., et al. Prediction Markets as Institutional Forecasting Support Systems[J]. Decision Support Systems, 2010,49(4):404-416
[65] Keen P. G. Decision Support Systems:An Organizational Perspective[J]. Lettere Al Nuovo Cimento, 1979,24(24):471-478
[66] Kohzadi N., Boyd M. S., Kermanshahi B., et al. A Comparison of Artificial Neural Network and Time Series Models for Forecasting Commodity Prices[J]. Neurocomputing, 1996,10(2):169-181
[67] Zhang G. P. Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model[J]. Neurocomputing, 2003,50(1):159-175
[68] Ghiassi M., Saidane H., Zimbra D. K. A Dynamic Artificial Neural Network Model for Forecasting Time Series Events[J]. International Journal of Forecasting, 2005,21(2):341-362
[69] Wang L., Zeng Y., Chen T. Back Propagation Neural Network with Adaptive Differential Evolution Algorithm for Time Series Forecasting[J]. Expert Systems with Applications, 2015,42(2):855-863
[70] Tseng F. M., Yu H. C., Tzeng G. H. Combining Neural Network Model With Seasonal Time Series ARIMA Model[J]. Technological Forecasting and Social Change, 2002,69(1):71-87
[71] Vapnik V. The Nature of Statistical Learning Theory[M]. New York:Springer-Verlag, 1995
[72] 安小米,马广惠,宋刚. 综合集成方法研究的起源及其演进发展[J]. 系统工程, 2018,36(10):1-13
[73] 于景元,周晓纪. 从定性到定量综合集成方法的实现和应用[J]. 系统工程理论与实践, 2002,22(10):26-32
[74] Wang S. Y. TEI@I:A New Nethodology for Studying Complex Systems[C]. The International Workshop on Complexity Science, Tsukuba, Japan, 2004
[75] Wang S. Y., Yu L. A. TEI@I:A New Methodology for Studying Volatility of International Oil Price[C]. The Open Conference of the International Research Team of AMSS on Complexity Science, Beijing, 2004
[76] Wang S. Y., Yu L. A., Lai K. K. Crude Oil Price Forecasting with TEI@I Methodology[J]. Journal of Systems Science and Complexity, 2005,18(2):145-166
[77] Yu L. A., Wang S., Lai K. K. Forecasting Crude Oil Price with an EMD-based Neural Network Ensemble Learning Paradigm[J]. Energy Economics, 2008,30(5):2623-2635
[78] Chai J., Liang T., Lai K. K., et al. The Future Natural Gas Consumption in China:Based on the LMDI-STIRPAT-PLSR Framework and Scenario Analysis[J]. Energy Policy, 2018,119:215-225
[79] 孙少龙. 基于"分解-聚类集成"学习范式的太阳辐射量预测技术研究及应用[D]. 兰州大学硕士学位论文, 2015
[80] 汪寿阳,余乐安,黎建强. TEI@I方法论及其在外汇汇率预测中的应用[J]. 管理学报, 2007,4(1):21-27
[81] Yu L. A., Wang S. Y., Lai K. K. Foreign-Exchange-Rate Forecasting with Artificial Neural Networks[M]. New York:Springer, 2007
[82] 张嘉为,索丽娜,齐晓楠,等. 基于TEI@I方法论的通货膨胀问题分析与预测[J]. 系统工程理论与实践, 2010,30(12):2157-2164
[83] Yan Y., Wei X., Hui B., et al. Method for Housing Price Forecasting Based on TEI@I Methodology[J]. Systems Engineering-Theory & Practice, 2007,27(7):1-9
[84] 郭琨,崔啸,王珏,等. "京十二条"房地产调控政策的影响——基于TEI@I方法论[J]. 管理科学学报, 2012,15(4):4-11
[85] 田歆,曹志刚,骆家伟,等. 基于TEI@I方法论的香港集装箱吞吐量预测方法[J]. 运筹与管理, 2009,18(4):82-89
[86] Tian X., Liu L., Lai K. K., et al. Analysis and Forecasting of Port Logistics Using TEI@I Methodology[J]. Transportation Planning and Technology, 2013,36(8):685-702
[87] Tian X., Lu X., Deng X. A TEI@I-Based Integrated Framework for Port Logistics Forecasting[C]. 2019 International Conference on Business Intelligence and Financial Engineering, 2009
[88] Yu L. A., Wang S. Y., Lai K. K., et al. Bio-Inspired Credit Risk Analysis——Computational Intelligence with Support Vector Machines[M]. Berlin:Springer-Verlag, 2008
[89] Yu L. A., Wang S. Y., Lai K. K. Computational Portfolio Selection[M]. Berlin:Springer-Verlag, 2008
[90] 田歆,汪寿阳,华国伟. 零售商供应链管理的一个系统框架与系统实现[J]. 系统工程理论与实践, 2009,29(10):45-52
[91] 汪寿阳,敖敬宁,乔晗,等. 基于知识给管理的商业模式冰山理论[J]. 管理评论, 2015,27(6):3-10
[92] Wang T., Tian X., Yu M., et al. Stage Division and Pattern Discovery of Complex Patient Care Processes[J]. Journal of Systems Science & Complexity, 2017,30(5):1136-1159
[93] 张茜,吴超,乔晗,等. 基于TEI@I方法论的中国季播电视综艺节目收视率预测[J]. 系统工程理论与实践, 2016,36(11):2905-2914
[94] Sun S., Wang S., Wei Y., et al. A Clustering-Based Nonlinear Ensemble Approach for Exchange Rates Forecasting[J]. IEEE, 2018,19(2):1-9
[95] Gan K., Sun S., Wang S., et al. A Secondary-decomposition-ensemble Learning Paradigm for Forecasting PM2.5 Concentration[J]. Atmospheric Pollution Research, 2018,9(6):989-999
[96] Zhang X., Yu L., Wang S., et al. Estimating the Impact of Extreme Events on Crude Oil Price:An EMD-based Event Analysis Method[J]. Energy Economics, 2009,31(5):768-778
[97] Sun S., Wei Y., Tsui K. L., et al. Forecasting Tourist Arrivals with Machine Learning and Internet Search Index[J]. Tourism Management, 2019,70(2):1-10
[98] 汤铃,余乐安,李建平,等. 复杂时间序列预测技术研究[M]. 北京:科学出版社, 2016
[99] 张珣,余乐安,黎建强,等. 重大突发事件对原油价格的影响[J]. 系统工程理论与实践, 2009,29(3):10-15
[100] Yu L., Wang Z., Tang L. A. Decomposition-ensemble Model with Data-characteristic-driven Reconstruction For Crude Oil Price Forecasting[J]. Applied Energy, 2015,156(10):251-267
[101] 汤铃,李建平,孙晓蕾,等. 基于模态分解的国家风险多尺度特征[J]. 管理评论, 2012,24(8):3-10
[102] Tang L., Yu L. A., He K. J. A Novel Data-characteristic-driven Modeling Methodology for Nuclear Energy Consumption Forecasting[J]. Applied Energy, 2014,128(9):1-14
[103] 柴建,张钟毓,李新,等. 中国航空燃油消费分析及预测[J]. 管理评论, 2016,28(1):11-21
[104] Chai J., Zhang Z. Y., Wang S. Y., et al. Aviation Fuel Demand Development in China[J]. Energy Economics, 2014,46(SI):224-235
[105] Chai J., Du M., Liang T., et al. Coal Consumption in China:How to Bend Down the Curve?[J]. Energy Economics, 2019,80(5):38-47
[106] Chai J., Xing L. M., Zhou X. Y., et al. Forecasting the WTI Crude Oil Price by a Hybrid-refined Method[J]. Energy Economics, 2018,71(3):114-127
[107] 张玲玲,房勇,杨涛,等. 管理科学与工程热点研究领域的文献计量分析[J]. 管理学报, 2005,2(4):379-385
[108] 李雪蓉,张晓旭,李政阳,等. 商业模式的文献计量分析[J]. 系统工程理论与实践, 2016,36(2):273-287
[109] Chen C., Hu Z., Liu S., et al. Emerging Trends in Regenerative Medicine:A Scientometric Analysis in Cite Space[J]. Expert Opinion on Biological Therapy, 2012,12(5):593-608
[110] Chen H., Chiang R. H. L., Storey V. C. Business Intelligence and Analytics:Form Big Data to Big Impact[J]. Mis Quarterly, 2012,36(4):1165-1188
[111] Lecun Y., Bengio Y., Hinton G. Deep Learning[J]. Nature, 2015,521(7553):436-444
[112] 胡毅,陈海强,齐鹰飞. 大数据时代计量经济学的新发展与新应用——第二届中国计量经济学者论坛(2018)综述[J]. 经济研究, 2019,54(3):199-203
[113] Hassani H., Silva E. S. Forecasting with Big Data:A Review[J]. Annals of Data Science, 2015,2(1):5-19
[114] 汪寿阳,洪永淼,霍红,等. 大数据时代下计量经济学若干重要发展方向[J]. 中国科学基金, 2019,33(4):386-393