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Establishing the Forecasting Model with Time Series Data Based on Graph and Particle Swarm Optimization

Journal of Computer Science Research 2023年第2期5卷 1-15页

作者： Le Thi Luong Tran Thi Thanh nghiem van tinh Bui Thi Thi Faculty of Electronic Thai Nguyen University of TechnologyThai Nguyen250000Vietnam

In recent years,a wide variety of fuzzy time series(FTS)forecasting models have been created and recommended to handle the complicated and ambiguous challenges relating to time series data from real-world ***,the accuracy of a model is problem-specific and varies across data *** a model’s precision varies between different data sets and depends on the situation at *** though many models assert that they are better than statistics and a single machine learning-based model,increasing forecasting accuracy is still a challenging *** the fuzzy time series models,the size of the intervals and the fuzzy relationship groups are thought to be crucial variables that affect the model’s forecasting *** study offers a hybrid FTS forecasting model that makes use of both the graph-based clustering technique(GBC)and particle swarm optimization(PSO)for adjusting interval lengths in the universe of discourse(UoD).The suggested model’s forecasting results have been compared to those provided by other current models on a dataset of enrollments at the University of *** all orders of fuzzy relationships,the suggested model outperforms its counterparts in terms of forecasting accuracy.

关键词： Forecasting FTS Fuzzy relationship group GBC Enrolments COVID-19

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