## 3 Overview of Results
### Country Analysis  
This section includes information about the countries with the highest number of publications and citations, noting also the collaboration between states. Figure 12 explores the main 10 countries that contributed the most to the development of time series forecasting and AI topics. The countries are classified based on the number of published papers, single-country publications (SCPs), and multiple-country publications (MCPs). In first place was China with 74 papers, which represented 28.5% of the total in articles published, with 57 SCPs (77%) and 17 MCPs (23%). In second place was Spain with only 17 articles, representing 6.5% of the total in articles published. In total, Spanish authors published 12 SCPs (70.6%) and five MCPs (29.4%). In third place was the United States of America (USA), with 16 publications (6.2% of total publications), comprising 13 SCPs (81.2%) and three MCPs (18.8%). In fourth place was Brazil, which released a total of 13 documents, representing 5% of the article total, with 11 SCPs (74.6%) and two MCPs (15.4%), followed by Italy with 10 papers (3.8% of total papers), including five SCPs (50%) and five MCPs (50%). The remaining countries each had less than 10 documents published \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Paper (Primary Author, Year, Journal, Reference)** & **Title** & **Data** & **Purpose** \\ \hline
6 & & Day-ahead hourly & & \\  & & photovoltaic power & & To implement short-term and \\  & & forecasting using & Historical data on & long-term temporal neural \\  & & attention-based & photovoltaic systems & network algorithms that \\  & _Energy_[63] & CNN-LSTM neural network & extracted from & predict the photovoltaic \\  & & embedded with multiple & DKASC website & power values based on \\  & & relevant and target & & historical data \\  & & variables prediction pattern & & \\ \hline
7 & & Forecasting energy & & To predict the energy \\  & & consumption time series & Real-time information & consumption of buildings \\  & _Energy_[57] & using machine learning & extracted from a smart & using machine learning \\  & & techniques based on usage & grid of & algorithms and based on \\  & & patterns of & an experimental building & past data \\  & & residential households & & \\ \hline
8 & & & An emotional ANN & & To define an emotional \\  & _Journal of_ & (EANN) approach to & Data from two watersheds & artificial neural network \\  & _Hydrology_[58] & modeling & have been selected & approach for daily \\  & & rainfall-runoff process & & rainfall runoff \\ \hline
9 & & & & To explore the neural \\  & & & network technique in traffic \\  & _Transportation_ & & management systems, in \\  & _Research Part C:_ & An urban traffic flow model & & order to be integrated into \\  & _Emerging_ & integrating neural networks & & a real-time adaptive urban \\  & _Technologies_[59] & & & traffic system if the results \\  & & & & are in accordance \\ \hline
10 & & & & with expectations \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._but were still worthy of mention: India with nine papers (3.5% of total articles), including eight SCPs (88.9%) and only one MCPs (11.1%); Iran with nine documents (3.5% of total articles), comprising six SCPs (66.7%) and three MCPs (33.3%); Korea with nine papers (3.5% of total articles), consisting of eight SCPs (88.9%) and one MCP (11.1%); the United Kingdom (UK) with eight documents (3.1% of total published documents), comprising four SCPs (50%) and four MCPs (50%); and Saudi Arabia with only seven publications (2.7% of total papers), consisting of four SCPs (57.1%) and three MCPs (42.9%). According to [PERSON] and [PERSON] [37], the country with the most articles published was China, with a total of 98 papers, 79 of them SCPs and 19 MCPs, followed by India with 55 papers, including 49 SCPs and six MCPs, while the USA was third with 38 articles, consisting of 31 SCPs and seven MCPs. Also in the top 10 were the UK, Korea, Australia, Germany, Spain, France, and Greece.