Establishing the Forecasting Model with Time Series Data Based on Graph and Particle Swarm Optimization
Establishing the Forecasting Model with Time Series Data Based on Graph and Particle Swarm Optimization DOI: https://doi.org/10.30564/jcsr.v5i1.5443 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 forec...