Methodology and its Applications in China

Transcription

1 Methodology and its Applications in China Shouyang Wang Center for Forecasting Science Chinese Academy of Sciences and

2 Outline Methodology -- A New Methodology Some Other Applications Conclusions

3 A A New Methodology for Forecasting

4 Introduction The methodology for crude oil price forecasting Simulation

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

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 The methodology for crude oil price forecasting A simulation study

10 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 Introduction (B) Here the name is based on text mining + econometrics + intelligence (intelligent integration. to replace + is to emphasize the functional of integrations. The general framework structure is shown in the following figure.

12 The general framework of

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 autoregression (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 backpropagation 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, y t + = f ( yt, yt 1,, 1 t p y )

19 ANN-based time series forecasting module

20 Bases and bases management module The other modules of the 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 webbased 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 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 forecasting approach

24 Introduction The 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 Simulation Results (I) The forecasting results of crude oil price (Jan Dec. 2003) Methods Criteria Full period ( ) Sub-period I (2000) Sub-period II (2001) Sub-period III (2002) Sub-period IV (2003) ARIMA ANN Simple integration Nonlinear integration RMSE D stat (%) RMSE D stat (%) RMSE D stat (%) RMSE D stat (%)

27 Simulation Results (II) The comparison of hit ratios between nonlinear integration approach and simple integration approach Methods Full period ( ) Subperiod I (2000) Subperiod II (2001) Subperiod III (2002) Subperiod 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%

28 Other Applications Forecasting of Foreign exchange Rates

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31 Other Applications Forecasting of China s Import and Export

32 2003 年初对上半年出口预测与实际比较 2003 年下半年出口预测与实际值比较 Jan-03 Feb-03 Mar-03 Apr-03 May-03 Jun 预测值 实际值 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 预测值 ( 亿美元 ) 实际值 ( 亿美元 )

33 Other Applications Forecasting of National Grain Output

34 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%)

35 Other Applications Forecasting of GDP s Growth Forecasting of CPI Forecasting of Housing Prices of 30 Large Cities in China Forecasting of Prices of Some Commodities Forecasting of FDI Forecasting of Demand in Industries, such as Logistics, Transportation, etc.

36 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. Some successful applications show that the methodology can be used to solve many types of hard forecasting problems.

37 Conclusions 3 TEI@I methodology needs more research, including more practical applications for improving the methodology). References [1] 余乐安 汪寿阳 黎建强, 外汇汇率预测和国际原油价价波动预测 --- TEI@I 方法论, 湖南大学出版社, 长沙,2006 [ 2 ] Lean Yu, Shouyang Wang and KK Lai, Forecasting of Foreign Exchange Rates, Springer, Boston, 2007

38 Thanks a lot! 例如 MADIS 外汇汇率预测网 ; MADIS 中国期货网 ; MADIS 国际原油价格波动预测研究网 例如 各种预测研究报告