Using grey Holt-Winters model to predict the air quality index for cties in China
The randomness, non-stationarity and irregularity of air quality index series bring the di fficulty of air quality index forecasting. To enhance forecast accuracy, a novel model combining grey accumulated generating technique and Holt-Winters method is developed for air quality index forecasting in this paper. The grey accumulated generating technique is utilized to handle non-stationarity of random and irregular data series and Holt-Winters method is employed to deal with the seasonal e ects. To verify and validate the proposed model, two monthly air quality index series from January in 2014 to December in 2016 collected from Shijiazhuang and Handan in China are taken as the test cases. The experimental results show that the proposed model is remarkably superior to conventional Holt-Winters method for its higher forecast accuracy.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link
Citation : Wu, L. et al. (2017) Using grey Holt-Winters model to predict the air quality index for cities in China. Natural Hazards, 88(2), pp.1003–1012.
ISSN : 0921-030X
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes