Comparison of improved hybrid FTS models for forecasting the urban air quality index

Abstract

Air pollution data is variable and vulnerable, and it is difficult to capture the changing pattern of air pollution data. In this study, multiple novel hybrid models are proposed which combine fuzzy time series (FTS) with mode decomposition to achieve stationarity and improve the effectiveness of the forecasting process. More specifically, air pollution data are first processed using the mode decomposition technique, and sample entropy is used to recombine the subseries, then, the information granularity is utilized for fuzzification. Complementary ensemble empirical mode decomposition-sample entropy-probabilistic weights FTS (CEEMD-SE-PWFTS) is the model that best captures the variation pattern of air pollution data. The experimental results verified that: Among all the proposed novel models, CEEMD-SE-PWFTS gets the highest forecasting accuracy of Air Quality Index (AQI). Air pollution datasets from five cities in China (Shanghai, Nanjing, Lianyungang, Beijing, and Guangzhou) were used for the tests, and the best MAPE in their prediction results were 2.78%, 3.43%, 5.61%, 3.63% and 2.31% respectively.