A Type-2 Fuzzy Time Series Model for Pattern Similarity Analysis: A Case Study on Air Quality Forecasting

Abstract

Air quality forecasting is a crucial part of megacities control and planning. It is necessary to develop high-quality forecasting methods in real-world applications, especially the issues that affect public health. Like many real-world problems, air quality is affected by uncertainties of nonstationary characteristics that their statistical attributes frequently change over time. Type-2 fuzzy systems have the capability to handle high-order uncertainties such as sequential dependencies associated with time series. This study develops a type-2 fuzzy intelligent system for air quality forecasting. Additionally, a dynamic time warping algorithm has been applied to estimate the pattern's similarity in long time series. The proposed model has been evaluated on a real dataset that contains the one-decade information about outdoor pollutants from April 2011 to November 2020 in Tehran and Beijing. The experiments have confirmed that the proposed model has lower forecasting error than counterpart models in terms of the RMSE, MAE, and MPE for both Tehran and Beijing time-series datasets. Also, the results confirm the superiority of the proposed model with an average area under the ROC curve (AUC) of 95% with a 95% confidence interval. This is promising for the prediction of air quality and make strategic prevention decisions.