Hybrid Fuzzy K-Medoids and Cat and Mouse-Based Optimizer for Markov Weighted Fuzzy Time Series

Abstract

This study seeks to test novel capabilities, specifically those of the hybrid fuzzy k-medoids (FKM) and cat and mouse-based optimizer (CMBO) partitioning approach, in overcoming the Markov weighted fuzzy time series (MWFTS) limitation in creating U talk intervals without fundamental standards. Researchers created a hybrid cat and mouse-based optimizer–fuzzy k-medoids (CMBOFKM) approach to be used with MWTS, since these limits may impair the accuracy of the MWFTS approach. Symmetrically, the hybrid method of CMBOFKM is an amalgamation of the FKM and CMBO methods, with the CMBO method playing a part in optimizing the cluster center of the FKM partition method to obtain the best U membership matrix value as the medoid value that will be used in the MWFTS’s fuzzification stage. Air quality data from Klang, Malaysia are used in the MWFTS–CMBOFKM technique. The evaluation of the model error values, known as mean absolute percentage error (MAPE) and root mean square error, yields the MWFTS–CMBOFKM evaluation findings that are displayed (RMSE). A 6.85% MAPE percentage and a 6071 RMSE score are shown by MWFTS–CMBOFKM using air quality data from Klang, Malaysia. The FKM partition approach can be hybridized with additional optimization techniques in the future to increase the MWFTS method’s precision.