## 3 Overview of Results
### Journal Analysis  
This sub-section focuses on the investigation of the main journals based on the number of published articles or citations. At the same time, a description of yearly scientific production and mean citations will be discussed. Figure 2 details the yearly scientific production on the time series forecast and AI domains, between 1997 and 2024. During the timespan, a positive trend could be observed, especially starting with the 2019-2020 period. The increase in the number of papers starting with this period could have two main reasons. The first one could be related to the mainstream adoption of the transformer models across various disciplines as well as \begin{table}
\begin{tabular}{l c} \hline \hline
**Indicator** & **Value** \\ \hline Timespan & 1997–2024 \\ Sources & 158 \\ Documents & 260 \\ Average years from publication & 3.91 \\ Average citations per documents & 20.51 \\ Co-authors per document & 4.08 \\ References & 11,079 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main information about extracted dataset. the creation of new transformer variants for time series forecasting, with impacts on the improvement of long-term forecasting accuracy. On the other hand, some of the increase could be due to the general trend observed in research by [PERSON] et al. [39], that underlined that the number of scientific papers had increased in recent decades. The increase in the number of publications could have multiple causes, such as, but not limited to, better automated data indexing observed through the use of various databases [39], the use of the keyword annotation scheme by research subjects [39], the advancements in computer science [39], the growth in research and development expenditures [40], and the overall number of researchers in the field [40]. The Annual Growth Rate was 23.11%, which points to the increased interest of researchers. It should be stated that the formula used for determining this indicator was the one offered by Biblioshiny [26,41]:  
\[\text{Annual Growth Rate}=\left(\frac{\text{Final Year Production}}{\text{ Initial Year Production}}\right)^{\frac{1}{\text{Time Span}}}-1\]  
in which the indicators are as follows:  
* Initial Year Production refers to the initial year for the latest period of the timespan in which all years have at least one publication
* Final Year Production refers to the final year for the latest period of the timespan in which all years have at least one publication
* Timespan refers to the period between the initial and the final year of the latest period of the timespan in which all years have at least one publication In the case depicted in Figure 2, the Annual Growth Rate was determined as follows:  
\[\text{Annual Growth Rate}=\left(\frac{2024\text{ Year Production}}{2009\text{ Year Production}}\right)^{\frac{1}{\text{Time Span between 2009 and 2003}}}-1\] \[=\left(\frac{64}{1}\right)^{\frac{1}{15}}-1=1.2311-1=0.2311=23.11\%\]  
The first paper was published in 1997, which represents 0.38% of total articles released. In 1998, no papers were published, followed by two consecutive years with papers published. Until 2018, the number of publications was around three or four papers per year, which represented approximately 1.5% of total publications. Starting in 2018, the number increased to six (2.31%), followed by seven in 2019 (2.69%). In 2020, a significant spike was registered, with 26 articles published (10%), followed by 41 in 2021 (15.77%), 40 in 2022 (15.38%), 50 in 2023 (19.23%), and a peak in 2024, with 64 documents (24.62%) published. [PERSON] and [PERSON] [42] evaluated the time series of papers using a bibliometric  
Figure 2: Annual Scientific Production. approach. The authors focused on papers published between 1960 and 2020, and the trend was similar, starting to increase in 2000, with a peak in 2020. [PERSON] et al. [43] presented the evolution of fuzzy-based time series forecasting, applying a bibliometric approach for articles that had been published between 2017 and 2021.