缺少数据
人工神经网络
计算机科学
元学习(计算机科学)
时间序列
学习迁移
趋同(经济学)
系列(地层学)
人工智能
机器学习
数据挖掘
工程类
生物
古生物学
经济
系统工程
任务(项目管理)
经济增长
作者
M. Maya,Wen Yu,Xiaoou Li
标识
DOI:10.1109/ssci50451.2021.9659864
摘要
In this paper, meta-learning and transfer-learning are combined to solve the missing data problem in time series forecasting. We successfully solved the common problems of neural networks based time series forecasting: local minimum and the poor predication accuracy with missing data. The strong and weak convergence of the proposed algorithms are analyzed. We successfully applied the proposed method to predict the air pollution of Mexico City with missing data. The prediction results and comparison show that the meta-transfer learning has very good prediction accuracy, when the air monitoring network has some failures.
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