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A Method for Multiple Periodic Factor Prediction Problems Using Complex Fuzzy Sets

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3 Author(s)
Jun Ma ; Decision Syst. & e-Service Intell. Lab., Univ. of Technol., Sydney, NSW, Australia ; Guangquan Zhang ; Jie Lu

Multiple periodic factor prediction (MPFP) problems exist widely in multisensor data fusion applications. Development of an effective prediction method should integrate information for multiple periodically changing factors. Because the uncertainty and periodicity coexist in the information used, the prediction method should be able to handle them simultaneously. In this study, complex fuzzy sets are used to represent the information with uncertainty and periodicity. A product-sum aggregation operator (PSAO) is developed for a set of complex fuzzy sets, which is used to integrate information with uncertainty and periodicity, and a PSAO-based prediction (PSAOP) method is then proposed to generate a solution of MPFP problems. This study illustrates the details of the PSAOP method through two real applications in annual sunspot number prediction and bushfire danger rating prediction. Experiments indicate that the proposed PSAOP method effectively handles the uncertainty and periodicity in the information of multiple periodic factors simultaneously and can generate accurate predictions for MPFP problems.

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Fuzzy Systems, IEEE Transactions on  (Volume:20 ,  Issue: 1 )