基于时间序列分析和卡尔曼滤波的霾预报技术

为了提高霾预报的准确率，解决时序模型的预测延时和准确率不高的问题，提出了一种基于时间序列分析和卡尔曼滤波相结合的混合霾预报算法。首先，利用图检验法和单位根检验法（ADF）检验时间序列的平稳性，通过差分运算将非平稳序列转化成平稳序列，对转化后的平稳序列进行建模；然后，将得到的模型方程作为卡尔曼滤波的状态方程和观测方程，依靠卡尔曼滤波递推性进行预报。实验结果表明，采用时间序列分析和卡尔曼滤波相结合的混合霾客观预报订正方法能有效提高霾预测精度。

Haze forecast based on time series analysis and Kalman filtering

In order to improve the accuracy of haze forecast and resolve the time lagging and low accuracy of temporal model, a mixed forecast method based on time series analysis and Karman filter was proposed. Firstly, the stability of time series was tested by graph analysis and eigenvalue analysis (ADF). Unstable time series were converted to stable ones by differential operation. A statistical function was established based on the stable time series. And then, the obtained model equations were used as the state and observation equation for Kalman filtering. Final haze forecast was based on recursion by Karman filtering. The experimental results showed that the accuracy of haze forecast is effectively improved by the mixed forecast method based on time series analysis and Karman filtering.