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TEI@I Methodology and its Applications

TEI@I Methodology and its Applications. Shouyang Wang Academy of Mathematics and Systems Science Chinese Academy of Sciences Center for Forecasting Science, Chinese Academy of Sciences Email: sywang@amss.ac.cn and yulean@amss.ac.cn http://madis1.iss.ac.cn and www.amss.ac.cn. Outline

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TEI@I Methodology and its Applications

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  1. TEI@I Methodologyand its Applications Shouyang Wang Academy of Mathematics and Systems Science Chinese Academy of Sciences Center for Forecasting Science, Chinese Academy of Sciences Email: sywang@amss.ac.cn and yulean@amss.ac.cn http://madis1.iss.ac.cn and www.amss.ac.cn

  2. Outline • TEI@I Methodology -- A New Methodology • Some Other Applications • Conclusions

  3. TEI@I—A New Methodology forCrude Oil Price Forecasting

  4. Introduction • The TEI@I methodology for crude oil price forecasting • A simulation study

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. Introduction • TheTEI@I methodologyfor crude oil price forecasting • A simulation study

  10. 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. .

  11. 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.

  12. The general framework of TEI@I

  13. 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.

  14. 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.

  15. The main process of WTM module

  16. 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.

  17. 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).

  18. 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,

  19. ANN-based time series forecasting module

  20. 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.

  21. Remarks • 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.

  22. 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.

  23. The scheme of the TEI@I forecasting approach

  24. Introduction • The TEI@I methodology for crude oil price forecasting • A simulation study

  25. 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.

  26. 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)

  27. 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

  28. Other Applications Forecasting of Foreign exchange Rates

  29. Other Applications Forecasting of China’s Import and Export

  30. 2003年下半年出口预测与实际值比较 2004年前三季度出口预测与实际值比较

  31. Other Applications Forecasting of National Grain Output

  32. 全国粮食产量预测 第一、预测提前期为半年以上。为政府有关部门安排粮食消费、储存和进出口留下了充足的时间； （国际上谷物产量预测提前期通常为2个月左右）。 第二、预测各年度的粮食丰、平、歉方向全部正确； （目前国际上发达国家预测谷物产量丰、平、歉方向为大部分正确） 第三、预测平均误差为产量的1.26% 。 (目前国际上发达国家预测误差为5-10%，如美国农业部提前2个月进行预测的误差为8-9%，法国最近6年的平均预测误差为9%)

  33. Other Applications Forecasting of FDI

  34. Other Applications Forecasting of CPI

  35. Other Applications Forecasting of Housing Prices

  36. Conclusions • A new TEI@I methodology integrating web-based text mining & rule-based expert system techniques, econometrical techniques with intelligent forecasting techniques is presented. Based on the TEI@I methodology, a novel nonlinear integrated forecasting approach is proposed. • The methodology .has been successfully applied to solve a number of hard forecasting problems in practice and the results are very encouraging. Our research supports some governmental departments for their policy making.

  37. Conclusions 3. The methodology can be used to solve many other complicated practical problems, not only in the field of forecasting. 4 However, TEI@I methodology needs more research, including on how to make a good integration for the three components.

  38. 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

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