基于注意力机制的城市多元空气质量数据缺失值填充.

Air pollution seriously affects human health and social sustainable development. However, the multi-variable air quality data obtained by sensors often have missing values, which brings difficulties to data analysis and processing. Currently, many analysis methods for changes in a certain air component only rely on time data and spatial data of this attribute, ignoring the influence of other air components on the trend of this attribute in the same time interval. In addition, it is difficult to achieve ideal results in filling discrete missing data. This paper proposes a Time Attention Model (TAM) based on deep learning, which uses attention mechanism to focus on the correlation between different timestamps and the correlation between different feature time series, and combines short-term historical data to fill missing values in multi-variable air quality data. The proposed model is evaluated using air quality data from Beijing, and the experimental results show that TAM has advantages over ten other baseline models. [ABSTRACT FROM AUTHOR]