An improved Fuzzy Time Series forecasting model based on Particle Swarm intervalization

The objective of this paper is to show the strength of a modified version of particle swarm optimization (PSO) in definition of suitable partitions of fuzzy time series forecasting and increasing its accuracy. Although a lot of contributions have been made to increase the quality of forecasts using fuzzy time series , there are only a few papers considering tuning the length of intervals in forecasting. In this paper, we propose a new method to tune the length of forecasting intervals and show the superiority of our procedure to those previously proposed using the well-known data of University of Alabama. The main contribution of this paper is to use a modified and effective PSO algorithm in which velocities are updated using a modified version of traditional PSO in order to have some diversification in solutions generated. In addition, monotonically decreasing functions for PSO parameters are used to improve the accuracy of forecast. The results show that our model outperforms other methods in the literature.