Abstract
Crude oil being a significant source of energy, change of crude oil price can affect the global economy. In this paper, a new approach based on the intuitionistic fuzzy set theory has been implemented to predict the crude oil price. This paper presents the intuitionistic fuzzy time series forecasting algorithm to enhance the efficacy of time series forecasting which includes fuzzy c-means clustering to obtain the optimal cluster centers. Further, a computational technique is proposed for the construction of triangular fuzzy sets and these fuzzy sets are converted to intuitionistic fuzzy sets with the help of Sugeno type intuitionistic fuzzy generator. The popular benchmark dataset of West Texas Intermediate crude oil spot price is used for the validation process. The numerical results when compared with existing methods notify that the proposed method enhances the accuracy of the crude oil price forecasts.
Highlights
Crude oil price is one of the major factors affecting the world’s economy
Hesitancy degree is used for the process of fuzzification and for different order of fuzzy logical relationships (FLRs), the process is evaluated
In this paper, a new approach based on intuitionistic fuzzy time series has been proposed to predict the price of crude oil
Summary
Crude oil price is one of the major factors affecting the world’s economy. Forecasting of crude oil prices has been gaining a lot of attention as these forecasts are very useful to industries, investors, government, household expenses and so on. A new reconstruction rule based on fuzzy clustering and dynamic time wrapping (DTW) was proposed by Chai et al (2019) using complete integration empirical mode decomposition algorithm and autoregressive integrated moving average (ARIMA). These traditional techniques lack when there is uncertainty in the historical data due to which Song and Chissom (1993) proposed the concept of fuzzy time series (FTS). An improved method of fuzzy time series forecasting based on IFS theory is proposed for the crude oil price prediction. The model is applied to the benchmark dataset of West Texas Intermediate (WTI) crude oil price and with the help of rootmean squared error (RMSE) and mean average percent error (MAPE), the accuracy of the model is determined
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