Abstract: Air pollution is one of the main problems of urban environmental governance, and PM2.5 is an important factor that affects air quality. In regard of problems that the traditional time series prediction model for PM2.5 concentration prediction lacks seasonal factor analysis and the prediction accuracy is not high enough, a fusion model based on machine learning, the Periodic Difference Automatic Smooth Regression (SARIMA)-Support Vector Machine (SVM) model is proposed in this paper. The fusion model is a tandem fusion model, which splits the data into linear parts and nonlinear parts. Based on the Autoregressive Integral Moving Average (ARIMA) model, the SARIMA model adds seasonal factor extraction parameters, which can effectively analyze the seasonal trend of PM2.5 concentration data and predict the future linear trend of the data. Combined with the SVM model, to optimize the residual sequence of the predicted data, using sliding step size prediction method to determine the optimal prediction step size for the residual series, the optimal model parameters are determined by grid search, which leads to the long-term prediction of PM2.5 concentration data and improves the overall prediction accuracy. By analyzing the PM2.5 concentration monitoring data in Wuhan in the past five years, the results shows that the prediction accuracy of the fusion model is greatly improved compared with the single model. In the same experimental environment, the accuracy of the fusion model is improved by 99%, 99% and 98% compared with the ARIMA, Auto ARIMA and SARIMA models respectively and the stability of the model is also better, which provides a new idea for the prediction of PM2.5 concentration.

摘要： 空气污染是城市环境治理的主要问题之一，而PM2.5是影响空气质量的重要因素。针对传统时间序列预测模型对PM2.5浓度预测缺少季节性因素分析，预测精度不够高的问题，本文提出了一种基于机器学习的融合模型——周期性差分自动平滑回归(SARIMA)-支持向量机(SVM)模型。该融合模型为串联型融合模型，将数据拆分为线性部分与非线性部分。SARIMA模型在自回归积分滑动平均(ARIMA)模型的基础上增加了季节因素提取参数，能有效分析PM2.5浓度数据的季节性规律变化趋势，较好的预测数据未来的线性变化趋势。结合SVM模型对预测数据的残差序列进行优化，利用滑动步长预测法确定残差序列的最优预测步长，通过网格搜索确定最优模型参数，实现对PM2.5浓度数据的长期预测，同时提高整体预测精度。通过对武汉市近5年的PM2.5浓度监测数据进行分析，结果表明该融合模型的预测精度相较于单一模型有很大提升，在相同的实验环境下对比单一的ARIMA、Auto ARIMA、SARIMA模型准确率分别提升了99%、99%、98%，模型稳定性也更好，为PM2.5浓度预测研究提供新的思路。