## 3 Overview of Results
### Journal Analysis
There was a positive trend between 2017 and 2020, similar to our findings, but in 2021, the value decreased (in a manner similar to what happened in 2022 in our dataset). According to the investigated research, there were similarities between our outcome and the existing literature reviews. [PERSON] et al. [38] described the AI impact on e-commerce, investigating the existing literature using a bibliometric analysis. The first paper was published in 1991, with a positive trend noted during the timespan. In 2019, a peak of 522 articles was achieved, followed by a small decrease in 2020 to 439 articles. [PERSON] and [PERSON] [37] presented the impact of AI on e-commerce and discovered that there was a positive trend in the publication of articles starting in 2016, peaking in 2021 with 149 papers, followed by a small decrease in 2022, to 146 articles, and a more significant decrease in 2023, to 73. Figure 3 illustrates the average citations per year on the domains of time series and AI. The first year with citations was 1997, with an average of 3.72 citations. A significant spike was observed in 2009, where the average citations per year reached 36.12, when the most cited article, with a total of 614 citations, was published by [PERSON] [44]. During the remaining period, the mean citations were between 1 and 7 per year. Figure 4 includes the most representative sources based on the number of publications. In first place was _IEEE Access_ with 11 papers published, followed by _Energies_ with nine documents, _Mathematics_ with seven documents, and _Applied Sciences-Basel_ with six articles. In fifth place, there were multiple journals: _Applied Energy, Energy, Knowledge-Based Systems, Sensors_, and _Sustainability_ with five papers each, while _Artificial Intelligence Review, Computational Economics, Soft Computing, Journal of Hydrology, Engineering Applications of Artificial Intelligence_, and _Expert Systems with Applications_ were the sources with four articles each. For a better understanding of the results, the existing articles that were published on similar topics were investigated. [PERSON] et al. [43] evaluated fuzzy-based time series forecasting using a bibliometric approach, discovering that _IEEE Access, Energies, Knowledge-Based Systems, Applied Sciences-Basel_, and _Applied Energy_ were among the journals with the highest impact on the investigated field. [PERSON] et al. [45] explored the AI impact on traffic flow prediction, where the time series method is one of the most implemented. The most significant journals identified were also among those in our findings: _Sustainability, Applied Sciences-Basel, Sensors_, and _IEEE Access_, but at the same time, there were sources that were not found in our results, such as _Neurocomputing_ or _Neural Computing & Applications_. Figure 5 shows the 10 most crucial local journals based on the number of citations. In first place are two journals, _Expert Systems with Applications_ and _Journal of Hydrology_, each with 289 citations, followed by _ARXIV_ with 225 citations, _Energy_ with 219 citations, and  
Figure 3: Average Citations per Year. Applied Energy_ with 216 citations. _IEEE Access_ had 175 citations and _International Journal of Forecasting_ had a total of 169 citations, followed by _Neurocomputing_ with 160 citations, _Energies_ with 153 citations, and _Applied Soft Computing_ with 150 citations. [PERSON] et al. [46] explored portfolio optimization using AI and discovered that _Expert Systems with Applications_ was the journal with the highest number of publications, followed by _Journal of Portfolio Management_ and _IEEE Access_. The rest of the journals had less impact and were not common in our outcome, demonstrating the variety of the domain and how the impact on the results changed if, during the extraction process, different filters were applied.