An air quality forecasting method using fuzzy time series with butterfly optimization algorithm

Air quality forecasting is an important application area of the time series forecasting problem. The successful prediction of the air quality of a place well in advance can able to help administrators to take the necessary steps to control air pollution. The administrator can also warn the citizens about the adverse effect of air pollution in advance. In this study, an air quality forecasting method is proposed to successfully forecast the air quality of a place. Here the type-2 fuzzy time series (FTS) forecasting method is applied to predict air quality. The performance of any FTS heavily depends on the selection of its hyperparameters. In this letter, a fuzzy time series optimization (FTSBO) algorithm is proposed to optimize all the hyperparameters of the FTS forecasting method. The proposed FTSBO algorithm originated from the butterfly optimization technique. In this work, the performance of the proposed forecasting method is also compared to the well-known forecasting methods. The simulation results established that the proposed forecasting method produces satisfactory performance, and its performance is better in comparison to other well-known forecasting methods.