Crude oil price forecasting using fuzzy time series

Predicting oil price movements is very important for investors. Fuzzy time series which combine people's subjective attitude and objective history values can help people to solve forecasting problems. It has been applied to many areas such as stock index, university enrollments, exchange rates and tourism forecasting. This paper brings fuzzy time series into short term crude oil price forecasting. We use West Taxes Intermediate oil as an example. To evaluate our method's performances, we use root mean square error method. Experiments show that fuzzy time series can produce good forecast results.

[1]  B. Chissom,et al.  Forecasting enrollments with fuzzy time series—part II , 1993 .

[2]  Ching-Hsue Cheng,et al.  High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets , 2008 .

[3]  H. Mohammadi,et al.  International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models , 2010 .

[4]  B. Chissom,et al.  Fuzzy time series and its models , 1993 .

[5]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[6]  Shi-Jinn Horng,et al.  Forecasting TAIFEX based on fuzzy time series and particle swarm optimization , 2010, Expert Syst. Appl..

[7]  Chao-Hung Wang,et al.  Constructing and applying an improved fuzzy time series model: Taking the tourism industry for example , 2008, Expert Syst. Appl..

[8]  R. Smyth,et al.  Cointegration between oil spot and future prices of the same and different grades in the presence of structural change , 2008, Energy Policy.

[9]  G. A. Miller.  THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[10]  Paolo Agnolucci,et al.  Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models , 2009 .

[11]  Cees Diks,et al.  The relationship between crude oil spot and futures prices: cointegration, linear and nonlinear causality , 2008 .

[12]  Michael McAleer,et al.  Market Efficiency of Oil Spot and Futures: A Mean-Variance and Stochastic Dominance Approach , 2010 .

[13]  Ching-Hsue Cheng,et al.  Adaptive-expectation based multi-attribute FTS model for forecasting TAIEX , 2010, Comput. Math. Appl..

[14]  James D. Hamilton,et al.  Understanding Crude Oil Prices , 2008 .

[15]  Shi-Jinn Horng,et al.  Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques , 2010, Expert Syst. Appl..

[16]  Ching-Hsue Cheng,et al.  Fuzzy time-series based on adaptive expectation model for TAIEX forecasting , 2008, Expert Syst. Appl..

[17]  Yungho Leu,et al.  A distance-based fuzzy time series model for exchange rates forecasting , 2009, Expert Syst. Appl..

[18]  Andrea Coppola,et al.  Forecasting oil price movements: Exploiting the information in the futures market , 2008 .

[19]  Myung-Geun Chun,et al.  TAIFEX and KOSPI 200 forecasting based on two-factors high-order fuzzy time series and particle swarm optimization , 2010, Expert Syst. Appl..