VWFTS-PSO: a novel method for time series forecasting using variational weighted fuzzy time series and particle swarm optimization

Variational weighted fuzzy time series models are known to perform better than traditional fuzzy time series models, due to their ability to handle outliers and noise. However, the performance of variational weighted fuzzy time series models is highly dependent on the partitioning of the universe of discourse. In this paper, we propose a novel variational weighted fuzzy model integrated with particle swarm optimization for choosing the best possible partitioning of the universe of discourse. Our approach combines the strengths of variational weighted fuzzy time series to handle uncertain and imprecise data and particle swarm optimization techniques to find the best model parameters. We evaluate the performance of the proposed approach on standard time series datasets and compare it with other forecasting methods in the literature. Our findings demonstrate that the proposed approach achieves accurate and reliable forecasts, outperforming other state-of-the-art approaches.