Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/11406
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dc.contributor.authorYusuf, Yakubu-
dc.contributor.authorMu'azu, Muhammed Bashir-
dc.contributor.authorAgajo, James-
dc.contributor.authorAbdullahi, Ibrahim Mohammed-
dc.date.accessioned2021-07-24T15:17:18Z-
dc.date.available2021-07-24T15:17:18Z-
dc.date.issued2018-05-
dc.identifier.citationYusuf Y., Mu’azu M. B., James A. & Ibrahim M. A. (2018), “Development Of An Optimized Forecasting Algorithm Using Particle Swarm Optimization (PSO) And K-Means Clustering Algorithm” Journal of Nigerian Association of Mathematical Physics, Volume 46 (May, 2018 Issue), pp177 –184.en_US
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/11406-
dc.description.abstractMost of the fuzzy forecasting methods based on fuzzy time series used arbitrary number of intervals and static length (same length) of intervals. The drawback of the arbitrary number of intervals and static length of intervals is that the historical data are roughly put into intervals, even if the variance of the historical data is not high. In this paper, we present optimized method for forecasting enrolments based on Fuzzy Time Series using Particle Swarm Optimization and K-Means clustering (PSO-KM). To verify the effectiveness of the proposed model, the empirical data for the enrolments of the University of Alabama was illustrated, and the experimental results show that the proposed model outperforms existing forecasting models with various orders and different interval lengths.en_US
dc.language.isoenen_US
dc.publisherJournal of Nigerian Association of Mathematical Physicsen_US
dc.relation.ispartofseries;46-
dc.subjectFuzzy time seriesen_US
dc.subjectParticle swarm optimization.en_US
dc.subjectforecastingen_US
dc.subjectK-means clusteringen_US
dc.titleDevelopment Of An Optimized Forecasting Algorithm Using Particle Swarm Optimization (PSO) And K-Means Clustering Algorithmen_US
dc.typeArticleen_US
Appears in Collections:Computer Engineering

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