  Accompanying rapid industrialization, humans are suffering from serious air
pollution problems. The demand for air quality prediction is becoming more and
more important to the government's policy-making and people's daily life. In
this paper, We propose GreenEyes -- a deep neural network model, which consists
of a WaveNet-based backbone block for learning representations of sequences and
an LSTM with a Temporal Attention module for capturing the hidden interactions
between features of multi-channel inputs. To evaluate the effectiveness of our
proposed method, we carry out several experiments including an ablation study
on our collected and preprocessed air quality data near HKUST. The experimental
results show our model can effectively predict the air quality level of the
next timestamp given any segment of the air quality data from the data set. We
have also released our standalone dataset at
https://github.com/AI-Huang/IAQI_Dataset The model and code for this paper are
publicly available at https://github.com/AI-Huang/AirEvaluation
