Imputation of Missing Data in Time Series for Air Pollutants Using Long Short-Term Memory Recurrent Neural Networks
Author:
Affiliation:
1. Anhui XinHua University, Hefei, China
2. Columbia University, NY, USA
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3267305.3274648
Reference16 articles.
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2. Dahe Jiang Yang Zhang Xiang Hu Yun Zeng Jianguo Tan and Demin Shao. 2004. Progress in developing an ANN model for air pollution index forecast. Atmospheric Environment 38 40(2004): 7055--7064. Dahe Jiang Yang Zhang Xiang Hu Yun Zeng Jianguo Tan and Demin Shao. 2004. Progress in developing an ANN model for air pollution index forecast. Atmospheric Environment 38 40(2004): 7055--7064.
3. Stanislaw Osowski and Konrad Garanty. 2007. Forecasting of the daily meteorological pollution using wavelets and support vector machine. Engineering Applications of Artificial Intelligence 20 6(2007): 745--755. 10.1016/j.engappai.2006.10.008 Stanislaw Osowski and Konrad Garanty. 2007. Forecasting of the daily meteorological pollution using wavelets and support vector machine. Engineering Applications of Artificial Intelligence 20 6(2007): 745--755. 10.1016/j.engappai.2006.10.008
4. Dynamic pre-training of Deep Recurrent Neural Networks for predicting environmental monitoring data
5. PM10 density forecast model using long short term memory
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