Wenxi Huang: a Hybrid Arima-svm Algorithm for Pm2.5 Concentration Prediction Using ..
نویسنده
چکیده
It is an important issue to study the prediction precision of Particulate Matter 2.5, PM2.5 (28 μg/m3), concentration change. The concentration of PM2.5 is influenced by many factors, and its change is characterized by non-linearity and randomness. This paper establishes a prediction model of PM2.5 concentration change to fit the nonlinear and random trend by combining Auto-Regressive Integrated Moving Average, ARIMA, model with Support Vector Machine, SVM, model to obtain a prediction result of PM2.5 concentration change. Specific PM2.5 concentration data are applied to test the performance of the model. Simulation results show that ARIMA-SVM improves the prediction accuracy of PM2.5 concentration change, reduces the prediction error, and depicts the change rule of its concentration more fully.
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