## 6 Experimental Setup and Results
### Cross-Source Performance
According to the findings outlined in Table 7, relying solely on INT or a combination of INT with Social Pulse or Milano Today does not yield the most favorable predictions. However, an improvement in accuracy ranging from 0.5% to 1% is noticeable when combining periodic data with weather conditions, as demonstrated in Table 6. This suggests that incorporating weather data can be a significant factor in enhancing the accuracy of predictions. In Table 8, we compare the performances of five distinct scenarios: solo INT, INT paired with news, INT integrated with periodic data, INT combined with news and periodic data, and fusion of all available data. It becomes evident from the table that including data linked to INT, such as news and periodic data, boosts the prediction accuracy. Furthermore, the results suggest that periodic data appear more beneficial than Milano Today, Social Pulse, and weather data, as the periodic patterns are naturally embedded within the INT data. It is important to highlight that integrating more relevant data does not trigger interference among the different data sources. Applying deep learning to identify correlations between these data sources enhances the model's overall performance. Table 8 compares the impact of different fusion strategies on the data, namely early and late fusion. Early fusion involves preprocessing the data into \(15\times 15\) grids and merging same-grid data before feeding them into the CNN + LSTM architecture. This process aims to capture the data's temporal and spatial characteristics. It is important to note that weather data were not included in the early fusion approach. The results show that early fusion, which integrates the data without extracting features, reduces the prediction accuracy, even when the data sources have some relation. This is likely due to the architecture's inability to capture all the data's attributes when combined without applying suitable feature extraction techniques. On the other hand, the late fusion approach improves \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **INT** & **INT+D** & **INT+W** & **INT+D+W** \\ \hline \multirow{2}{*}{Task Max} & MA (\%) & 73.6 & 75.1 & 77.1 & **79.1** \\ \cline{2-6}  & RMSE & 206.28 & 199.96 & 154.36 & **78.98** \\ \hline \multirow{2}{*}{Task Avg} & MA (\%) & 78.9 & 79.2 & 83.8 & **84.2** \\ \cline{2-6}  & RMSE & 116.45 & 116.35 & 71.24 & **65.84** \\ \hline \multirow{2}{*}{Task Min} & MA (\%) & 72.8 & 73.5 & 78.4 & **80.5** \\ \cline{2-6}  & RMSE & 133.56 & 128.35 & 101.35 & **74.48** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Compare the impact of the input period. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **INT** & **INT+E** & **INT+S+E** & **INT+M+E** & **INT+S+M+E** & **INT+D+E** & **INT+W+E** & **INT+D+W+E** \\ \hline \multirow{2}{*}{Task Max} & MA (\%) & 73.6 & 72.9 & 74.5 & 74.8 & 75.3 & 75.5 & 77.6 & **80.9** \\ \cline{2-6}  & RMSE & 206.28 & 209.35 & 204.32 & 203.95 & 199.63 & 198.67 & **151.24** & 173.25 \\ \hline \multirow{2}{*}{Task Avg} & MA (\%) & 78.9 & 78.5 & 79.9 & 80.1 & 81.2 & 79.6 & 83.6 & **85.6** \\ \cline{2-6}  & RMSE & 116.45 & 118.36 & 114.69 & 113.63 & 109.54 & 115.32 & 69.32 & **64.37** \\ \hline \multirow{2}{*}{Task Min} & MA (\%) & 72.8 & 72.5 & 77.5 & 77.8 & 78.6 & 74.1 & 79.2 & **81.9** \\ \cline{2-6}  & RMSE & 133.56 & 135.24 & 107.35 & 106.32 & 99.79 & 124.35 & 92.35 & **67.12** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the weather effect. the accuracy of the prediction. In this approach, the neural network performs feature extraction and identifies correlations within the data. The results show that late fusion outperforms early fusion in terms of prediction accuracy. This highlights the importance of neural-network-based feature extraction in enhancing fusion results, as it provides a deeper understanding of the data's characteristics and correlations.