## 5 Experiments
### Experimental Results
#### 5.3.3 Ablation Study of Hybrid Predictor  
We study the advantages of our hybrid predictor in this section. Hence, we test our proposed hybrid predictor with the ablation analysis and verify whether our overall \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Metrics** & \(T_{0}\) & \(T_{1}-T_{6}\) & \(T_{7}-T_{12}\) & \(T_{13}-T_{18}\) & \(T_{19}-T_{24}\) \\ \hline \multirow{3}{*}{k-NN Nearest Average} & MAE & 23.19 & 32.01 & 45.21 & 53.25 & 58.05 \\  & RMSE & 40.87 & 52.82 & 71.41 & 81.46 & 85.92 \\ \cline{1-1} \cline{2-7}  & MAE & 22.80 & 31.77 & 45.08 & 53.43 & 57.94 \\ \cline{1-1} \cline{2-7}  & RMSE & 40.24 & 52.44 & 71.25 & 81.33 & 85.78 \\ \cline{1-1} \cline{2-7}  & MAE & 21.41 & 25.70 & 29.90 & 32.54 & 34.04 \\ \cline{1-1} \cline{2-7}  & RMSE & 37.33 & 41.32 & 45.90 & 49.83 & 51.10 \\ \cline{1-1} \cline{2-7}  & MAE & 24.93 & 29.00 & 35.14 & 39.09 & 40.30 \\ \cline{1-1} \cline{2-7}  & RMSE & 39.15 & 43.68 & 51.42 & 57.14 & 58.38 \\ \cline{1-1} \cline{2-7}  & MAE & 24.95 & 26.64 & 31.54 & 33.74 & 36.02 \\ \cline{1-1} \cline{2-7}  & RMSE & 43.28 & 45.22 & 52.23 & 56.89 & 59.91 \\ \cline{1-1} \cline{2-7}  & MAE & 52.66 & 53.73 & 57.30 & 60.90 & 62.53 \\ \cline{1-1} \cline{2-7}  & RMSE & 88.60 & 91.33 & 97.18 & 102.53 & 103.55 \\ \cline{1-1} \cline{2-7}  & MAE & 22.80 & 31.87 & 42.43 & 51.29 & 57.85 \\ \cline{1-1} \cline{2-7}  & RMSE & 40.24 & 52.61 & 68.72 & 77.48 & 81.22 \\ \cline{1-1} \cline{2-7}  & MAE & 19.35 & 19.41 & 19.72 & 20.41 & 22.25 \\ \cline{1-1} \cline{2-7}  & RMSE & 33.78 & 32.08 & 33.14 & 35.88 & 39.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between the proposed and the baseline models. The values in the table are the MAE and RMSE of AQI values. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Metrics** & \(T_{0}\) & \(T_{1}-T_{6}\) & \(T_{7}-T_{12}\) & \(T_{13}-T_{18}\) & \(T_{19}-T_{24}\) \\ \hline \multirow{3}{*}{Without Attention} & MAE & 21.53 & 20.88 & 22.00 & 22.12 & 24.91 \\  & RMSE & 36.74 & 35.40 & 37.35 & 39.94 & 45.75 \\ \cline{1-1}  & MAE & 20.42 & 19.67 & 20.68 & 21.21 & 23.14 \\ \cline{1-1}  & RMSE & 33.28 & 31.84 & 33.26 & 36.12 & 40.65 \\ \cline{1-1} \cline{2-7}  & MAE & 19.35 & 19.41 & 19.72 & 20.41 & 22.25 \\ \cline{1-1} \cline{2-7}  & RMSE & 33.78 & 32.08 & 33.14 & 35.88 & 39.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation analysis on Inversed Distance Attention (IDA) networks. The values in the table are the MAE and RMSE of AQI values. framework can achieve the best performance. First, we compare our performance with different individual predictors. The first one is MLP as the time-independent predictor, and the second one is the LSTM AutoEncoder as the time-dependent predictor. Namely, we divide our framework into two data flows to predict the result separately in this experiment. Second, we combine these two predictors into a hybrid predictor as a testing model with a simple sum. We sum up the results of two predictors with equal weight. Finally, we use the proposed dynamic weights sum to aggregate the results and compare them to the results of the summation that has no dynamic weights sum. In this part, we show our proposed idea step by step with the results. However, we expect a prediction model might have better performance during the current time than the more extended periods. That is to say, the trend of the error from \(T_{0}\) to \(T_{19-24}\) should be incremental, meeting our expectations for real life. From Tables 3 and 4, we only show the results with MAE to compare each predictor, since the trends of MAE and RMSE are similar. We list all 10 experiment results simultaneously for a more detailed comparison between two different predictors. The results with the MLP predictor (Table 3) are obviously superior in that the overall MAE is better than the results with the LSTM AutoEncoder predictor (Table 4), whether in every experiment or the average results. However, the accuracy trend of the LSTM AutoEncoder predictor is more realistic than the MLP predictor.