## 5 Experiments
### Experimental Results
#### 5.3.3 Ablation Study of Hybrid Predictor
We mark the values in \(T_{0}\) that are worse than \(T_{1-6}\) in Tables 3 and 4 and denote them as unexpected \(T_{0}\), and find that the number of unexpected \(T_{0}\) in Table 3 is more than in Table 4. Thus, we can have a \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. of Data** & \(\mathbf{T_{0}}\) & \(\mathbf{T_{1}-T_{6}}\) & \(\mathbf{T_{7}-T_{12}}\) & \(\mathbf{T_{13}-T_{18}}\) & \(\mathbf{T_{19}-T_{24}}\) \\ \hline Data 1 & 25.41 & 25.59 & 28.45 & 31.00 & 33.14 \\ Data 2 & 31.70 & 27.86 & 25.01 & 26.77 & 32.33 \\ Data 3 & 28.73 & 29.01 & 31.15 & 34.45 & 43.32 \\ Data 4 & 27.00 & 28.76 & 30.65 & 34.68 & 37.84 \\ Data 5 & 25.52 & 27.91 & 33.87 & 33.67 & 34.52 \\ Data 6 & 28.28 & 27.64 & 31.20 & 33.95 & 37.78 \\ Data 7 & 30.75 & 30.88 & 30.54 & 29.28 & 29.05 \\ Data 8 & 33.25 & 31.23 & 34.69 & 39.48 & 42.04 \\ Data 9 & 28.00 & 26.77 & 27.81 & 28.80 & 32.11 \\ Data 10 & 22.77 & 22.97 & 25.32 & 25.77 & 29.80 \\ \hline AVERAGE & 28.14 & 27.86 & 29.87 & 31.78 & 35.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The MAE with only LSTM AutoEncoder as the predictor for all 10 times. The values in the table are the MAE of AQI values. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. of Data** & \(\mathbf{T_{0}}\) & \(\mathbf{T_{1}-T_{6}}\) & \(\mathbf{T_{7}-T_{12}}\) & \(\mathbf{T_{13}-T_{18}}\) & \(\mathbf{T_{19}-T_{24}}\) \\ \hline Data 1 & 18.53 & 18.50 & 20.79 & 19.82 & 21.85 \\ Data 2 & 22.11 & 20.14 & 19.23 & 19.09 & 20.30 \\ Data 3 & 24.41 & 22.54 & 23.63 & 23.40 & 29.16 \\ Data 4 & 17.88 & 18.31 & 18.90 & 20.48 & 23.59 \\ Data 5 & 18.68 & 21.77 & 26.85 & 22.76 & 24.47 \\ Data 6 & 23.28 & 19.83 & 22.08 & 23.17 & 25.53 \\ Data 7 & 23.80 & 23.19 & 25.07 & 23.80 & 24.80 \\ Data 8 & 22.13 & 19.76 & 19.97 & 23.97 & 23.45 \\ Data 9 & 19.81 & 17.73 & 18.78 & 20.05 & 21.54 \\ Data 10 & 18.02 & 17.61 & 18.79 & 19.39 & 22.16 \\ \hline \hline AVERAGE & 20.86 & 19.94 & 21.41 & 21.59 & 23.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The MAE with only MLP as the predictor for all 10 times. The values in the table are the MAE of AQI values. hypothesis that each predictor structure has its advantages and disadvantages, and their combined effectiveness proves that their results are complementary. Hence, the aggregation of two data flows shows that the two predictors are complementary and beneficial to our proposed method. Table 5 shows the result from integrating two predictors by simply adding up. Through simple summation, the overall MAE is better than with each predictor individually, and the number of unexpected \(T_{0}\) is less than the results in Table 3. Thus, we easily confirm that a simple sum hybrid predictor can enhance the performance and that separating the predicted values into two independent data flows is effective. Last but not least, we assume that the proportions of the MLP predictor and LSTM AutoEncoder predictor might not be the same for all cases. Hence, we propose to sum up the dynamic weights in our model and show the results in Table 6. We exploit the MAE of 10 groups data and the overall MAE and RMSE to compare the difference between simple sum in Table 5 and dynamic weights sum in Table 6. Even though the number of unexpected \(T_{0}\) is the same, the overall MAE and RMSE have both improved with the dynamic weights sum. In conclusion, our proposed hybrid predictor with dynamic weights sum efficiently reduces overall errors and can meet the demand in real life as well.