Open AccessPM2.5 Analysis and Prediction Based on Seasonal Time Series Model
Author(s) -
Xiqin Ao,
Hongwu Yuan,
Dianya Zhang
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/371/5/052006
Subject(s) - time series , statistical software , statistical analysis , series (stratigraphy) , seasonality , environmental science , statistics , meteorology , climatology , mathematics , geography , geology , paleontology
The PM2.5 data in Hefei area from 2014 to 2018 were collected through the website of meteorological bureau, and the data were analyzed and modeled with the help of statistical analysis software SPSS and EVIEWS. Firstly, the annual and seasonal distribution characteristics and trend of PM2.5 data in Hefei area during the past five years were analyzed. It was concluded that the distribution of PM2.5 had periodic characteristics, and the air quality in summer was the best and that in winter was the worst. According to the results of statistical analysis, PM2.5 data had seasonal characteristics. On this basis, the Holt-Winters additive model and SARIMA model in seasonal time series model were selected to model and predict. The prediction values of the two models were compared with the real values. The results showed that the prediction accuracy of the Holt-Winters additive model was slightly higher than that of the SARIMA model.