论文标题
一个新型的混合框架，用于使用Ceemdan和深度暂时卷积神经网络的小时PM2.5浓度预测
A Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network
论文作者
论文摘要
对于每小时的PM2.5浓度预测，准确捕获影响PM2.5浓度变化的外部因素的数据模式并构建预测模型是提高预测准确性的有效手段之一。在这项研究中，开发了一种基于完整的集合经验模式分解的新型混合预测模型（Ceemdan）（Ceemdan）和深层卷积神经网络（DEEPTCN）是通过建模历史污染物浓度数据，气象学数据和离散时间变量数据的数据模型来预测PM2.5浓度的。将北京浓度为样本，实验结果表明，与时间序列模型，人工神经网络和流行的深度学习模型相比，提出的Ceemdan-DeeptCN模型的预测准确性被证实是最高的。新模型提高了对PM2.5相关因子数据模式进行建模的能力，并可以用作预测PM2.5浓度的有前途的工具。
For hourly PM2.5 concentration prediction, accurately capturing the data patterns of external factors that affect PM2.5 concentration changes, and constructing a forecasting model is one of efficient means to improve forecasting accuracy. In this study, a novel hybrid forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and deep temporal convolutional neural network (DeepTCN) is developed to predict PM2.5 concentration, by modelling the data patterns of historical pollutant concentrations data, meteorological data, and discrete time variables' data. Taking PM2.5 concentration of Beijing as the sample, experimental results showed that the forecasting accuracy of the proposed CEEMDAN-DeepTCN model is verified to be the highest when compared with the time series model, artificial neural network, and the popular deep learning models. The new model has improved the capability to model the PM2.5-related factor data patterns, and can be used as a promising tool for forecasting PM2.5 concentrations.
