基于烛台图模式匹配的PM_(2.5)扩散特征的提取

Extraction of PM_(2.5)diffusion characteristics based on candlestick pattern matching

现有大气质量预测方法多基于单纯的时间序列数据进行趋势预测,忽略了污染物传输和扩散规律及其分类间模式特征的问题。为此,提出一种基于烛台图模式匹配(CPM)的PM_(2.5)(大气细颗粒物污染)扩散特征提取方法。首先,利用基于卷积神经网络(CNN)的卷积思想从大量历史PM_(2.5)序列中生成基础周期烛台图;然后,通过距离公式对不同烛台图特征向量的浓度模式进行聚类分析;最后,结合CNN在图像识别中的独特优势,形成融合图形特征与时序特征序列的混合模型,判断带有反转信号的烛台图将导致的趋势反转情况。在桂林市大气质量在线监测站的监测时序数据集上的实验结果表明,与使用单一时间序列数据的深度卷积神经网络VGG(Visual Geometry Group)相比,基于CPM的提取方法准确率提升了1.9个百分点。可见,基于CPM的方法能有效提取PM_(2.5)趋势特征,可以用于预测未来污染物浓度周期变化。

Most existing air quality prediction methods focus on simple time series data for trend prediction,and ignore the pollutant transport and diffusion laws and corresponding classified pattern features.In order to solve the above problem,a PM_(2.5)diffusion characteristic extraction method based on Candlestick Pattern Matching(CPM)was proposed.Firstly,the basic periodic candlestick charts from a large number of historical PM_(2.5)sequences were generated by using the convolution idea of Convolutional Neural Network(CNN).Then,the concentration patterns of different candlestick chart feature vectors were clustered and analyzed by using the distance formula.Finally,combining the unique advantages of CNN in image recognition,a hybrid model integrating graphical features and time series features sequences was formed,and the trend reversal that would be caused by candlestick charts with reversal signals was judged.Experimental results on the monitoring time series dataset of Guilin air quality online monitoring stations show that compared with the VGG(Visual Geometry Group)-based method which uses the single time series data,the accuracy of the CPM-based method is improved by 1.9 percentage points.It can be seen that the CPM-based method can effectively extract the trend features of PM_(2.5)and be used for predicting the periodic change of pollutant concentration in the future.