Sugeno Intuitionistic Fuzzy Generator Based Computational Technique for Crude Oil Price Forecasting

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.

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A fuzzy time series forecasting method induced by intuitionistic fuzzy sets

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962315500415 ◽

2015 ◽

Vol 06 (04) ◽

pp. 1550041 ◽

Cited By ~ 7

Author(s):

Sanjay Kumar ◽

Sukhdev Singh Gangwar

Keyword(s):

Time Series ◽

Fuzzy Sets ◽

Time Series Data ◽

Financial Time Series ◽

Intuitionistic Fuzzy Sets ◽

Time Series Forecasting ◽

Fuzzy Time Series ◽

Series Data ◽

Intuitionistic Fuzzy ◽

Forecasting Method

Intuitionistic fuzzy sets (IFSs) are well established as a tool to handle the hesitation in the decision system. In this research paper, fuzzy sets induced by IFS are used to develop a fuzzy time series forecasting model to incorporate degree of hesitation (nondeterminacy). To improve the forecasting accuracy, induced fuzzy sets are used to establish fuzzy logical relations. To verify the performance of the proposed model, it is implemented on one of the benchmarking time series data. Further, developed forecasting method is also tested and validated by applying it on a financial time series data. In order to show the accuracy in forecasting, the method is compared with other forecasting methods using different error measures.

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INTUITIONISTIC FUZZY SETS BASED METHOD FOR FUZZY TIME SERIES FORECASTING

Cybernetics & Systems ◽

10.1080/01969722.2012.637014 ◽

2012 ◽

Vol 43 (1) ◽

pp. 34-47 ◽

Cited By ~ 43

Author(s):

Bhagawati P. Joshi ◽

Sanjay Kumar

Keyword(s):

Time Series ◽

Fuzzy Sets ◽

Intuitionistic Fuzzy Sets ◽

Time Series Forecasting ◽

Fuzzy Time Series ◽

Intuitionistic Fuzzy

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Intuitionistic Fuzzy Time Series Forecasting Based on Dual Hesitant Fuzzy Set for Stock Market

Exploring Critical Approaches of Evolutionary Computation - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-5832-3.ch003 ◽

2019 ◽

pp. 37-57

Author(s):

Sanjay Kumar ◽

Kamlesh Bisht ◽

Krishna Kumar Gupta

Keyword(s):

Time Series ◽

Fuzzy Sets ◽

Fuzzy Set ◽

Time Series Data ◽

Time Series Forecasting ◽

Fuzzy Time Series ◽

Hesitant Fuzzy Set ◽

Series Data ◽

Intuitionistic Fuzzy ◽

Dual Hesitant Fuzzy Set

In this chapter, an application of dual hesitant fuzzy set (DHFS) in intuitionistic fuzzy time series forecasting is proposed to handle fuzziness and non-determinism that occurs due to multiple valid fuzzification method for time series data. Advantages of the proposed DHFS-based time series forecasting method are that it includes characteristics of both intuitionistic and hesitant fuzzy sets to handle the non-determinism and hesitancy corresponding to single membership grade multiple membership grades of an element. In the present study, universe of discourse is partitioned and fuzzified the time series data by two different fuzzification methods (triangular and Gaussian) to construct DHFS. Further, elements of DHFS are aggregated to construct the intuitionistic fuzzy sets. Proposed method is implemented over the share market prizes of SBI at BSE, India and SENSEX of BSE to confirm its out performance over existing time series forecasting methods using RMSE and AFER.

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