ARIMA模型结合小波去噪的贵阳城市交通流预测

A Hybrid ARIMA and Wavelet Denoising Model for Traffic Flow Prediction of Guiyang City

由于交通流量具有非线性和强干扰性的特征,在不同的时频域空间具有不同的特性;本文首先应用小波分析的方法,将含有综合信息的一组原始交通流信号分解为多组特征不同的时间序列信号,再利用ARIMA模型良好的线性拟合能力,将经过小波分析的时间信号通过ARIMA模型进行处理。利用Matlab和SPSS,对实测交通流数据进行了验证分析,实验表明,小波分析结合ARIMA模型预测的方法能有效的降低预测误差,具有很高的可行性。

As the traffic flow has the features of nonlinear and strong interference, it has different characteristics in different time-frequency spaces. Firstly, the wavelet analysis method was used to decomposes a group of original traffic flow signals containing summarized information into series of time sequence signals that have different characteristics, then good linear fitting ability of the ARIMA model was utilized to process the wavelet analysis time signal through the ARIMA model. Using matlab and SPSS, the measured traffic flow data were analyzed and verified. Experiment results show that the way of combining the wavelet analysis with ARIMA model can reduce the prediction error effectively, and improve the forecasting accuracy by about 80%, this way has high feasibility.