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Abstract A hybrid prediction model was established to implement the time series forecasting of traffic accident statistical index based on the ARIMA model and the SVR model. The ARIMA model was used to complete the linear fitting of original time series, with the residual error of the ARIMA model then transformed into fuzzy information granulation particles made up of Low, R and Up. An SVR model was developed to describe the seasonal trend of the residual error with Low, R and Up as input. The predicted value of the ARIMA model was fixed based on the SVR regression result of the seasonal residual error, with the predicted value of the hybrid model being calculated. Empirical research results show that the accuracy of the hybrid model is higher than that of the single ARIMA model and that the seasonal trends of empirical time series are precisely represented by fuzzy information granulation particles.
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| Keywords
accident prediction
time series
ARIMA
fuzzy information granulation
SVR
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Issue Date: 15 March 2014
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2014,
Vol. 54
),Xun JI1,Liang ZHU2
