## 3 Overview of Results
### Mixed Analysis  
This section focuses on the thematic evolution and analysis, noting the major topics that have been discussed by the academic community, at the same time highlighting the  
Figure 14: Country Collaboration Map. \begin{table}
\begin{tabular}{c c} \hline
**Country** & **Number of Papers** \\ \hline China & 42 \\ \hline USA & 26 \\ \hline UK & 21 \\ \hline Spain & 20 \\ \hline Saudi Arabia & 20 \\ \hline Iran & 15 \\ \hline India & 12 \\ \hline Turkey & 12 \\ \hline France & 11 \\ \hline Canada & 10 \\ \hline Egypt & 10 \\ \hline Serbia & 10 \\ \hline Tunisia & 9 \\ \hline Lebanon & 9 \\ \hline Brazil & 8 \\ \hline Iraq & 8 \\ \hline Italy & 7 \\ \hline United Arab Emirates & 7 \\ \hline Malaysia & 6 \\ \hline Ireland & 6 \\ \hline \end{tabular}
\end{table} 
Table 4: Countries with Most Papers published. most frequently used Keywords Plus and author's keywords. A detailed analysis of collaboration networks for the 30 most representative authors has been performed. Figure 15 presents the 150 most used Keywords Plus grouped in multiple circles, based on the thematic context. In order to obtain the graph below, the minimum cluster frequency was set to 27. Two metrics are available in the thematic map: centrality, which expresses the impact of the keywords in the thematic context, investigating the external associations; and the density, which shows the degree of development, exploring the internal association of keywords in various thematic contexts [64]. The most representative cluster, colored in blue, on the bottom right part of the graph with a high centrality and a reduced density, contains information about ML algorithms and time series methods such as \"prediction\" (52 occurrences), \"models\" (41 occurrences), \"models\" (25 occurrences), \"neural-networks\" (40 occurrences), \"algorithm\" (20 occurrences), \"optimization\" (20 occurrences), \"system\" (19 occurrences), \"time-series\" (18 occurrences), and \"regression\" (16 occurrences). In the middle of the graph is a cluster colored in orange, with a medium density and centrality, referring to the ML networks: \"machine\" (10 occurrences) and \"networks\" (8 occurrences). The last cluster, in the top left part of the graph and highlighted in pink, contains two of the most used time series algorithms: \"lstm\" (8 occurrences) and \"arima\" (7 occurrences). Figure 16 includes the 150 most representative keywords used by the main authors, grouped in four different themes. The minimum cluster frequency setting was set to 25. In the bottom right part of the graph is the most representative circle, highlighted in orange, referring to machine learning integration with time series and AI, featuring a high centrality and small density. Figure 15: Thematic Map of Keywords Plus. The most used keywords in the orange cluster are \"artificial intelligence\" (93 occurrences), \"time series forecasting\" (67 occurrences), \"machine learning\" (41 occurrences), \"deep learning\" (40 occurrences), \"time series\" (17 occurrences), \"neural networks\" (15 occurrences), \"COVID-19\" (13 occurrences), \"explainable artificial intelligence\" (11 occurrences), \"artificial neural network\" (9 occurrences), and \"lstm\" (8 occurrences). The second most relevant cluster is colored in pink and presents the time series analysis, together with forecasting, containing the following keywords: \"forecasting\" (39 occurrences), \"time series\" (17 occurrences), \"time series analysis\" (14 occurrences), \"predictive models\" (11 occurrences), \"lstm\" (8 occurrences), and \"recurrent neural network\" (8 occurrences). The last two clusters, colored in red and pink, have a relatively small centrality value, while the red circle has a higher density compared to the pink circle. The focus on the red circle is \"time-series forecasting\" (21 occurrences) and \"neural network\" (6 occurrences), while the pink circle contains \"explainable artificial intelligence\" (11 occurrences). [PERSON] et al.