The FCM Algorithm based High-order Intuitionistic Fuzzy Time Series Forecasting Model

Abstract

Abstract: A new high-order intuitionistic fuzzy time series forecasting model is proposed in this paper to aiming at the shortcomings of the existing fuzzy time series forecasting models with effective universe of discourse partitioning and fuzzification preprocessing of historical data.The model firstly combines two partitioning methods of equal universe of discourse partitioning and non-equal universe of discourse partitioning based on the FCM algorithm, which better reflects the correlation characteristics of the internal or local patterns of historical data. On this basis, considering the historical data characteristics of intuitionistic fuzzy time series, a more objective method is proposed for intuitionistic fuzzy processing of historical data, which better reflects the fuzzy state of "neither this nor that" of historical data. At the same time, the intuitionistic fuzzy trend approximation factor is used to describe the membership of historical data to fuzzy sets instead of the traditional membership function, then combined with "max-min" aggregation operation, the fuzzy states to be considered are selected reasonably, and the forecasting results are defuzzied. In the experimental part, the model uses the enrollment of students in the University of Alabama and gold futures closing price as the experimental data, and compares the forecasting results with those of some existing models to verify the feasibility and superiority of the new model.

Key words: intuitionistic fuzzy time series, FCM algorithm, intuitionistic fuzzy set, fuzzy rule, forecasting

CLC Number:

SONG Min, BAI Yu, LIU Shihu. The FCM Algorithm based High-order Intuitionistic Fuzzy Time Series Forecasting Model[J]. Journal of Qujing Normal University, 2022, 41(6): 1-13.

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