DEVELOPMENT OF A FUZZY TIME SERIES MODEL USING CAT SWARM OPTIMIZATION CLUSTERING AND OPTIMIZED WEIGHTS OF FUZZY RELATIONS

DEVELOPMENT OF A FUZZY TIME SERIES MODEL USING CAT SWARM OPTIMIZATION CLUSTERING AND OPTIMIZED WEIGHTS OF FUZZY RELATIONS

ABSTRACT

This research developed a hybrid forecasting technique that integrates Cat Swarm Optimization Clustering (CSO-C) and Particle Swarm Optimization (PSO) algorithms with Fuzzy Time Series (FTS) forecasting model. Cat Swarm Optimization Clustering (CSO-C) which is an algorithm for data classification is adopted at the fuzzification stage to objectively partition the universe of discourse into unequal intervals. Then, disambiguated fuzzy relationships are obtained using Fuzzy Set Grouping (FSG). Finally, Particle Swarm Optimization (PSO) was adopted to optimize the defuzzification phase; by tuning weights assigned to fuzzy sets in a rule. This rule is a fuzzy logical relationship induced from a fuzzy set group (FSG). The clustering and optimization algorithms were implemented in MATLAB. Belgium road yearly accident data, Alabama University yearly student enrolment data, Taiwan future exchange data, University of Maiduguri (UNIMAID) yearly student enrolment data and Jigawa state yearly temperature data were collected and used to evaluate the developed hybrid model. To evaluate the forecasting efficiency of the developed hybrid model, its statistical performance metric of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were calculated and compared with previous techniques in the literature. Improvement was achieved in the developed forecasting technique, when compared with the benchmark Fuzzy Time Series (FTS) model of Qiang Song and Brad S. Chissom part I and II in forecasting student enrolment of University of Alabama. Results showed that an RMSE of 6.669 and MAPE result of 0.033%was obtained when compared with the benchmark work of Song and Chissom in student enrolment whose result was an RMSE of 650 and MAPE of 3.22%. There is also an improvement, in comparison to Fuzzy CMeans FTS based model of Yusuf et al (2015) whose result showed an RMSE of 7.02 and MAPE of 0.04%. The application of developed model on Belgium car road accident obtained an RMSE result of 5.931 and MAPE result of 0.346%which is an improvement over FCM based FTS model with RMSE of 19.2 and MAPE of 0.67%.Similarly, on application an RMSE of 2.571 and MAPE of 0.0375%were obtained in the forecast of University of Maiduguri student enrolment while in Jigawa monthly temperature forecast RMSE of 0.357 and MAPE of 0.1% were obtained. Relatively, the points on the plots followed a steady trend with the actual values for enrolment and temperature forecast respectively.

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