Download
e management sciences and tei@i methodology n.
Skip this Video
Loading SlideShow in 5 Seconds..
e-Management Sciences and TEI@I Methodology PowerPoint Presentation
Download Presentation
e-Management Sciences and TEI@I Methodology

e-Management Sciences and TEI@I Methodology

229 Vues Download Presentation
Télécharger la présentation

e-Management Sciences and TEI@I Methodology

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. e-Management Sciences and TEI@I Methodology Chengdu, May 12, 2005 Shouyang Wang Academy of Mathematics and Systems Science Chinese Academy of Sciences GS School of Management,Chinese Academy of Sciences Email: sywang@amss.ac.cn http://madis1.iss.ac.cn and www.amss.ac.cn

  2. Outline • Some Development Trends/Areas of Management Sciences • A New Methodology for Studying Complex Management Systems ---A Case of Crude Oil Price Forecasting • Conclusions and Discussions • Issues on Publication in Journals

  3. A. Trends/Areas of MSs In the wide sense of MSs INFORMS IFORS IDS American Academy of Management

  4. A. Trends/Areas of MSs (1) Operations management of virtual enterprises/organizations

  5. A. Trends/Areas of MSs (2) MIS(DSS) Based on Internet/Intranet; Mobile based GDSS

  6. A. Trends/Areas of MSs (3) e-Auctions; e-Bidding; e-Negotiations

  7. A. Trends/Areas of MSs (4) e-Logistics

  8. A. Trends/Areas of MSs (5) e-Maintenance/Replacement

  9. A. Trends/Areas of MSs (6) Supply Chain Management Special Issue: supply chain management in cultural industry

  10. A. Trends/Areas of MSs (7) Complex Networks: Design and Operations

  11. A. Trends/Areas of MSs (8) Online Risk Management 《参考消息》(Oct.13,2004): MIT与反恐 Online Financial Risk Management

  12. A. Trends/Areas of MSs (9) Transportation Management Telcommunication Management Financial Management Revenue Management Service Industry!!!

  13. A. Trends/Areas of MSs (10) Soft Computing

  14. A. Trends/Areas of MSs (11) Integration of Various MS Techniques; Integration with Other Technologies MS Research Methodologies

  15. B. A New Methodology TEI@I—A New Methodology forCrude Oil Price Forecasting Shouyang Wang and Lean Yu

  16. Outline B. A New Methodology • Introduction • The TEI@I methodology for crude oil price forecasting • A simulation study • Concluding remarks

  17. Introduction I • Importance of oil price forecasting: The role of oil in the world economy becomes more and more significant because nearly two-thirds of the world’s energy consumption comes from the crude oil and natural gas. For example, • worldwide consumption of crude oil exceeds $500 billion, roughly 10% of the USA’s GDP. • crude oil is also the world’s most actively traded commodity, accounting for about 10% of total world trade.

  18. Introduction II • Determination of oil price : Basically, crude oil price is determined by its supply and demand, and is strongly influenced by many irregular future events like the weather, stock levels, GDP growth, political aspects and even people’s expectation. • The above facts lead to a strongly fluctuating and interacting market whose fundamental mechanism governing the complex dynamics is not well understood. • Furthermore, because sharp oil price movements are likely to disturb aggregate economic activity, researchers have shown considerable interests for volatile oil prices. • Therefore, forecasting oil prices is an important and very hard topic due to its intrinsic difficulty and practical applications.

  19. Introduction III • Main literature about oil price forecasting: • Watkins, G.C., Plourde, A.: How volatile are crude oil prices? OPEC Review, 18(4), (1994) 220-245. • Hagen, R.: How is the international price of a particular crude determining? OPEC Review, 18 (1), (1994) 145-158 • Stevens, P.: The determination of oil prices 1945-1995. Energy Policy, 23(10), (1995) 861-870 • Huntington, H.G.: Oil price forecasting in the 1980s: what went wrong? The Energy Journal, 15(2), (1994) 1-22. • Abramson, B., Finizza, A.: Probabilistic forecasts from probabilistic models: a case study in the oil market. International Journal of Forecasting, 11(1), (1995) 63-72 • Morana, C.: A semiparametric approach to short-term oil price forecasting. Energy Economics, 23(3), (2001) 325-338

  20. Introduction IV • Evaluation about literature: • There are only very limited number of related papers on oil price forecasting. • The literature focuses on the oil price volatility analysis. • The literature focuses only on oil price determination within the framework of supply and demand. • It is, therefore, very necessary to introduce new method for crude oil price forecasting.

  21. Outline B. A New Methodology • Introduction • TheTEI@I methodologyfor crude oil price forecasting • A simulation study • Concluding remarks

  22. TEI@I Introduction (A) • In view of difficulty and complexity of crude oil price forecasting, a new methodology named TEI@I is proposed in this study to “integrate” systematically “text mining”, “econometrics” and “intelligent techniques” and a novel integrated forecasting approach with error correction and judgmental adjustment within the framework of the TEI@I methodology is presented for improving prediction performance. .

  23. TEI@I Introduction (B) • Here the name “TEI@I” is based on “text mining” + “econometrics” + “intelligence (intelligent algorithms)” @ “integration”. Using “@” to replace “+” is to emphasize the functional of integrations.The general framework structure is shown in the following figure.

  24. The general framework of TEI@I

  25. Man-machine interface (MMI) module • The man-machine interface (MMI) is a graphical window through which users can exchange information within the framework of TEI@I. • it handles all input/output between users and the TEI@I system. • it can be considered as open platform communicating with users and interacting with other components of the TEI@I system.

  26. Web-based text mining module • Crude oil market is an unstable market with high volatility and oil price is often affected by many related factors. • In order to improve forecasting accuracy, these related factors should be taken into consideration in forecasting. • Web-based text mining is used to explore the related factors. • In this study, the main goal of web-based text mining module is to collect related information affecting oil price variability from Internet and to provide the collected useful information to the rule-based expert system forecasting module.

  27. The main process of WTM module

  28. Rule-based expert system (RES) module • Expert system module is used to transform the irregular events into valuable adjusted information. • That is, rule-based expert system is used to extract some rules to judge oil price abnormal variability by summarizing the relationships between oil price fluctuation and key factors affecting oil price volatility. • See the paper for a detailed discussion.

  29. Econometrical forecasting module • It includes a large number of modeling techniques and models, such as autoregressive integrated moving average (ARIMA) model, vector auto-regression (VAR) model, generalized moment method (GMM), etc. • Autoregressive integrated moving average (ARIMA) model is used here. • ARIMA is used to model the linear pattern of oil price time series, while nonlinear component is modeled by artificial neural network (ANN).

  30. ANN-based time series forecasting module • The ANN used in this study is a three-layer back-propagation neural network (BPNN) incorporating the Levenberg- Marquardt algorithm for training. • For an univariate time-series forecasting problem, the inputs of the network are the past lagged observations of the data series and the outputs are the future values. • BPNN time-series forecasting model performs a nonlinear mapping. That is,

  31. ANN-based time series forecasting module

  32. Bases and bases management module • The other modules of the TEI@I system have a strong connection with this module. • For example, ANN-based forecasting module utilizes MB and DB, while the rule-based expert system mainly used the KB and DB. • To summarize, the TEI@I system framework is developed through an integration of the web-based text mining, rule-based expert system and ANN-based time series forecasting techniques.

  33. Summary • In this framework, econometrical models (e.g., autoregressive integrated moving average (ARIMA)) are used to model the linear components of crude oil price time series (i.e., the main trends). • Nonlinear components of crude oil price time series (i.e., error term) are modeled by a neural network (NN) model. • the effects of irregular and infrequent future events on crude oil price are explored by web-based text mining (WTM) and rule-based expert systems (RES) techniques. • MMI and BBM are the auxiliary modules for constructing the integrated TEI@I system.

  34. The nonlinear integrated forecasting approach • Within the framework of TEI@I methodology, a novel nonlinear integrated forecasting approach is proposed to improve oil price forecasting performance. • The flow chart of the nonlinear integrated forecasting approach is shown in the following.

  35. The scheme of the TEI@I forecasting approach

  36. Outline B. A New Methodology • Introduction • The TEI@I methodology for crude oil price forecasting • A simulation study • Concluding remarks

  37. A simulation study Data and settings • The crude oil price data used in this study are monthly spot prices of West Texas Intermediate (WTI) crude oil, covered the period from January 1970 to December 2003 with a total of n = 408 observations. For the purpose of this study, the first 360 observations are used in-sample data (including 72 validation data) as training and validating sets, while the reminders are used as testing ones.

  38. Methods Criteria Full period (2000-2003) Sub-period I (2000) Sub-period II (2001) Sub-period III (2002) Sub-period IV (2003) ARIMA RMSE 2.3392 3.0032 1.7495 1.9037 2.4868 Dstat(%) 54.17 41.67 50.00 58.33 66.67 ANN RMSE 2.3336 2.7304 1.4847 1.8531 2.6436 Dstat(%) 70.83 75.00 75.00 66.67 66.67 Simple integration RMSE 2.0350 3.2653 1.0435 0.9729 1.9665 Dstat(%) 85.42 75.00 91.67 100.00 75.00 Nonlinear integration RMSE 1.0549 1.7205 0.6834 0.8333 0.5746 Dstat(%) 95.83 100.00 83.33 100.00 100.00 Simulation Results (I) The forecasting results of crude oil price (Jan. 2000 - Dec. 2003)

  39. Methods Full period (2000-2003) Sub-period I (2000) Sub-period II (2001) Sub-period III (2002) Sub-period IV (2003) Simple integration 70.83% 41.67% 83.33% 91.67% 66.67% Nonlinear integration 85.42% 83.33% 75.00% 83.33% 100.0% Simulation Results (II) The comparison of hit ratios between nonlinear integration approach and simple integration approach

  40. Outline B. A New Methodology • Introduction • The TEI@I methodology for crude oil price forecasting • A simulation study • Concluding remarks

  41. Concluding Remarks • In this study, a new TEI@I methodology integrating web-based text mining & rule-based expert system techniques, econometrical techniques with intelligent forecasting techniques is proposed for crude oil price forecasting. Based on the TEI@I methodology, a novel nonlinear integrated forecasting approach is presented. • The simulation results show that the proposed nonlinear integrated forecasting approach with error correction and judgmental adjustment produces a definite improvement in oil price forecasting; • The nonlinear integrated forecasts has shown superior to the simple integrated forecasts and the individual forecasts. • The novel nonlinear integrated forecasting model can be used as an alternative tool for crude oil price forecasting to obtain better forecasting accuracy than before.

  42. C. Conclusions and Discussions (1) Some trends of MSs developments are discussed in this talk, but it should be mentioned that there are many other important and promising research areas/directions in MSs; The features of them: (e) + (I); TEI@I methodology needs more research, including study of its principles and its practice (especially needs more applications for improvements) ;

  43. C. Conclusions and Discussions (2) China has achieved many great achievements in MSs research and education, unfortunately they are not discussed in this talk; Choice of a research area/direction should be made based on his/her research interest, conditions, etc.

  44. 谢谢!欢迎提问与讨论! http://madis1.iss.ac.cn, 例如 MADIS外汇汇率预测网; MADIS中国基金网; MADIS国际原油价格波动预测研究网 MADIS系列政策研究报告或摘要 MADIS国际期刊论文全文pdf和中文期刊论文首页 MADIS近期开发的DSSs和MISs的主界面 ISI的SCI期刊排名表和SSCI期刊排名表\NSFC管理期刊表 MADIS会议消息和国际期刊Call for Papers

  45. D 管理科学研究要注意的问题与原则 不同的分支领域的研究方法往往是不同的,但也有一些需要共同遵循的原则,例如 • 文献资料要充分、对国际学科前沿和发展动态要有系统的了解(自己做研究前,最好写一个综述);(书尚和李刚的例子) • 研究的问题要抓得准,做到解决用户需求与学术创新相结合;(在解决问题中提出新概念、新理论或新方法等) • 数据的收集要能支持研究工作,尽可能做到全并进行必要的处理;(贸易和外汇黑市汇率的预测为例) • 模型的适用性务必进行检验,研究中注意假设(假定)是否合理;(金融市场的有效性研究为例) • 多一种工具或方法,就是你比别人有优势的地方; • 结论基于深入的分析,而不是凭空想象的结果;

  46. D 管理科学研究要注意的问题与原则 • 结果发表前最好请他人多评论,使得研究工作尽可能完美;(国外通常的做法:working papers + seminar presentations) • 保证与用户的交互,真正解决用户的问题和需求;(以二炮的项目为例) • 部分得意的工作应该争取在国际著名期刊上发表; • 不要期待一个人解决所有的问题,team work; • 重视定量分析的同时,也重视定性分析的重要性。复杂的管理问题的解决常是定性与定量分析相结合的结果;切勿无根据的分析和为模型而模型的研究; • 重视名家的工作和阅读著名期刊上的论文,可能的话索取working papers(时间上短1-3年时间)。

  47. E 关于论文发表的几个议题 1 对期刊的整体情况不了解; 2 国外著名大学与研究机构对各类论文的认识; 3 期刊与论文的影响评价; 4 信心不够的问题; 5 “好”与“不好”?选择合适的期刊为最高原则; 6 国内外有哪些期刊值得推荐? 7 如何投稿以及与期刊的主编打交道? 8 英文写作困难如何解决?

  48. E 关于论文发表的几个议题 1 对期刊的整体情况不了解 自己研究领域中有哪些重要学术期刊? 自己研究领域中拿5种期刊是最好的(top 5 journals)? 自己研究领域中10个最活跃的学者(MASs)常在哪5种期刊上发表研究结果? SCI(Science Citation Index)的JCR中“有MS期刊”250余种; SSCI(Social Science Citation Index)的JCR中“有MS期刊”400余种; 国家自然科学基金委员会管理科学部推荐了20种期刊; 中国科学院文献情报中心“中国引文数据库”中也公布了1个目录; 各个大学的管理学院基本上都有1个单子! SCI&SSCI.PDF

  49. E 关于论文发表的几个议题 2 国外著名大学与研究机构对各类论文的认识 所了解的等级是 A articles in top 5 or 10 journals; B other refereed journal articles; C refereed book chapters; D refereed proceedings articles; E articles presented at series conferences 当然,不能一概而论! 但如果在国外找工作的话,这是非常重要的!

  50. E 关于论文发表的几个议题 3 期刊与论文的影响评价 有多种评价方法和相应的指标体系,但文献计量学的方法从统计学上来认识更具有理论根据,因此受到广泛的重视。 影响因子; 被引用次数; hot articles 期刊与论文的关系(统计意义下的) SCI journals 与 top 5(10)journals