TEI@I Methodologyand its Applications

Shouyang WangShouyang 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 TEI@I Methodology -- A New

Methodology

Some Other Applications

Conclusions

TEI@I—A New TEI@I—A New Methodology Methodology

forforCrude Oil Price ForecastingCrude Oil Price Forecasting

Introduction

The TEI@I methodology for

crude oil price forecasting

A simulation study

Introduction I

Importance of oil price forecastingImportance 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.

Introduction II

Determination of oil price 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.

Introduction III

Main literature about oil price forecasting: 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

Introduction IV

Evaluation about literature: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 It is, therefore, very necessary to introduce

new method for crude oil price forecasting.new method for crude oil price forecasting.

Introduction

The TEI@I methodology for

crude oil price forecasting

A simulation study

TEI@I Introduction (A)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. .

TEI@I Introduction (B)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.

The general framework of TEI@I

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.

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.

The main process of WTM module

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.

Econometrical forecasting moduleEconometrical 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).

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, ),,,( 11 ptttt yyyfy

ANN-based time series forecasting moduleANN-based time series forecasting module

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.

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.

Remarks

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.

The scheme of the TEI@I forecasting approach

Introduction

The TEI@I methodology for crude oil

price forecasting

A simulation study

A simulation study

Data and settingsThe 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.

The forecasting results of crude oil price (Jan. 2000 - Dec. 2003)

Simulation Results (I)

Methods CriteriaFull period

(2000-2003)Sub-period I

(2000)Sub-period II

(2001)

Sub-period III

(2002)

Sub-period IV

(2003)

ARIMARMSE 2.3392 3.0032 1.7495 1.9037 2.4868

Dstat (%) 54.17 41.67 50.00 58.33 66.67

ANNRMSE 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 (II)

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%

The comparison of hit ratios between nonlinear integration approach and simple integration approach

Forecasting of Foreign exchange Rates

Other Applications

Other Applications

Forecasting of China’s Import and Export

2003年初对上半年出口预测与实际比较

0

50

100

150

200

250

300

350

400

Jan-03 Feb-03 Mar-03 Apr-03 May-03 Jun-03

预测值 实际值

0

100

200

300

400

500

600

2003. 7 2003. 8 2003. 9 2003. 1 2003. 11 2003. 12

预测值（亿美元） 实际值（亿美元）

0

100

200

300

400

500

600

2004. 1 2004. 2 2004. 3 2004. 4 2004. 5 2004. 6 2004. 7 2004. 8 2004. 9

预测值（亿美元） 实际值（亿美元）

2004年前三季度出口预测与实际值比较

2003年下半年出口预测与实际值比较

Other Applications

Forecasting of National Grain Output

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%)

Other Applications

Forecasting of FDI

Other Applications

Forecasting of CPI

Other Applications

Forecasting of Housing Prices

Conclusions

1. 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.

2. 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.

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.

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