基于Prophet-随机森林优化模型的空气质量指数规模预测

长时间的规模预测有助于从宏观角度分析事物的发展趋势与规律。对上海市2013—2017年逐日空气质量指数(AQI)进行分析,在此基础上建立了Prophet-随机森林(RF)优化模型。Prophet模型将AQI时间序列趋势分解为趋势项、季节项、节假日效应;RF算法用于弥补Prophet模型无法预测随机非线性部分的缺点,对Prophet模型进行优化,将Prophet-RF优化模型用于AQI的规模预测。结果表明:相比于Prophet模型,Prophet-RF优化模型的预测效果更加精确,其中,拟合值的均方根误差和平均绝对误差均减少了0.161,预测值的均方根误差和平均绝对误差分别减少了0.434和0.399。Prophet-RF优化模型解释性强且精度高,对于时间序列的规模预测具有较明显的优势。

Scale prediction of AQI based on Prophet-random forest optimization model

The long-term scale prediction helps to analyze the development trend and law of things from a macroscopic perspective. Prophet-random forest (RF) optimization model was established on the analysis of the Shanghai daily air quality index (AQI) from2013 to2017. The Prophet model decomposed the AQI time series trend into growth items,seasonal and holiday effects. The RF model was used to make up for the defect that the Prophet model could not predict the stochastic nonlinear part,and to optimize the Prophet model. Prophet-RF optimization model was applied to forecast the scale of the AQI. The experimental results showed that compared to the single Prophet model,the prediction results of Prophet-RF optimization model were more accurate. Among them,the root mean square error and mean absolute error of fitting values both decreased by 0.161 . Root mean square error and mean absolute error of predictive value decreased by 0.434 and 0.399,respectively. Prophet-RF optimization model had higher precision and was explanatory,having obvious advantages to predict the scale of time series.