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

Authors

  • Le Thi Luong

    Faculty of Electronic, Thai Nguyen University of Technology, Thai Nguyen, 250000, Vietnam
  • Tran Thi Thanh

    Faculty of Electronic, Thai Nguyen University of Technology, Thai Nguyen, 250000, Vietnam
  • Nghiem Van Tinh

    Faculty of Electronic, Thai Nguyen University of Technology, Thai Nguyen, 250000, Vietnam
  • Bui Thi Thi

    Faculty of Electronic, Thai Nguyen University of Technology, Thai Nguyen, 250000, Vietnam

DOI:

https://doi.org/10.30564/jcsr.v5i2.5443
Received: 2 February 2023 | Revised: 24 March 2023 | Accepted: 27 March 2023 | Published Online: 13 April 2023

Abstract

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 sources. However, the accuracy of a model is problem-specific and varies across data sets. But a model's precision varies between different data sets and depends on the situation at hand. Even though many models assert that they are better than statistics and a single machine learning-based model, increasing forecasting accuracy is still a challenging task. In 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 abilities. This 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 Alabama. For all orders of fuzzy relationships, the suggested model outperforms its counterparts in terms of forecasting accuracy.

Keywords:

Forecasting; FTS; Fuzzy relationship group; GBC; Enrolments; COVID-19

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How to Cite

Luong, L. T., Thanh, T. T., Tinh, N. V., & Thi, B. T. (2023). Establishing the Forecasting Model with Time Series Data Based on Graph and Particle Swarm Optimization. Journal of Computer Science Research, 5(2), 1–15. https://doi.org/10.30564/jcsr.v5i2.5443

Issue

Vol. 5 , Iss. 2 (April 2023)

Article Type

Article

License

Copyright © 2023 Author(s)

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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