均方误差
人工神经网络
人工智能
计算机科学
希尔伯特-黄变换
滞后
时间序列
数据建模
边距(机器学习)
平均绝对误差
预测建模
数据集
机器学习
模式识别(心理学)
统计
数学
计算机网络
滤波器(信号处理)
数据库
计算机视觉
作者
Jingyi Zhao,Fahu He,Zhanlin Ji,Иван Ганчев
标识
DOI:10.1109/csci54926.2021.00104
摘要
Most of the research, conducted to date on the prediction of Fine Particulate Matter with a diameter less than 2.5 micrometers (PM2.5), based on machine learning and deep learning techniques, ignores the fact that the PM2.5 values are constantly changing over time. Although many researchers use Long Short-Term Memory (LSTM) neural networks based on time series to predict PM2.5 values, due to the instability of data, the results often had a certain lag. This paper♠ proposes to use the combined Empirical Mode Decomposition (EMD)—LSTM fusion model for the prediction of PM2.5 values. To evaluate the performance of the model in comparison to other existing models, experiments were conducted with a public PM2.5 data set, using the root mean square error (RMSE) and mean absolute error (MAE) as metrics. The results confirm the superiority of the combined EMD-LSTM model.
科研通智能强力驱动
Strongly Powered by AbleSci AI