## 3 Overview of Results
### Mixed Analysis
[51] performed a bibliometric analysis of the main themes of AI in the leading scientific journals during the COVID-19 pandemic, discovering that the main authors' keywords were clustered in three groups, with the first cluster focusing on \"training\", \"deep learning\", and \"classification\", the second cluster presenting \"functions\", \"uncertainty\", and \"neural networks\", and the third cluster containing information about \"robots\", \"neurons\", and \"computer simulation\". There are similarities between the existing literature and our results, COVID-19 being one of the main factors that contributed to the development of the fields, together with \"neural networks\" and \"deep learning\", according to [PERSON] et al. [51]. At the same time, there are significant differences, which is normal, thanks to the different databases that were used, different software tools, different topics, and different time periods. Figure 16: Thematic Map of Authors’ Keywords. Figure 17 presents the thematic evolution for Keywords Plus, divided into three different periods: 1997-2015, 2016-2020, and 2021-2024. Between 1997 and 2015, the main topics discussed in the contexts of time series and AI were related to \"neural networks\" and \"model\", while, starting in 2016, they evolved to \"prediction\", \"particle swarm optimization\", \"classification\", \"market\", \"models\", and \"artificial neural network\", demonstrating the development of the domains, making it more versatile. Between 2021 and 2024, the main focus of the researchers was on improving the prediction, since numerous models had been defined between 2016 and 2019. At the same time, the \"artificial neural network\" remained a key topic, together with \"system\" and \"network\". In their research, [PERSON] et al. [65] defined the most used AI keywords between 2000 and 2020. Initially, the majority of terms were related to \"optimization\", \"prediction\", \"models\", and \"neural networks\", while researcher interest shifted during the timespan to a broader variety of topics, such as \"complexity\", \"agents\", \"prediction\", \"architecture\", and \"behavior\". Figure 18 includes a factorial analysis of the most frequently used 100 Keywords Plus, grouped in three clusters. The blue cluster contains the majority of terms, which define the applicability of time series and AI: \"wavelet transform\", \"empirical mode decomposition\", \"optimization\", \"power\", \"system\", \"impact\", \"runoff\", \"river\", \"streamflow\", and \"reservoir system\", while the red cluster focuses on \"artificial neural networks\", \"genetic algorithms\", and \"forecasting enrollments\". The last cluster, highlighted in green, encapsulates the strategies that should be taken into account: \"optimal operation\", \"basin\", \"synchronization\", \"strategies\", and \"intelligence\". Articles that investigated the time series forecasting and AI domains were evaluated to compare the results with the existing literature, and [PERSON] et al. [65] grouped the keywords into multiple clusters, with one focusing on \"wavelet transform\", \"decomposition\", \"memory\", and \"validation, another cluster exploring \"genetic algorithms\", \"regression\", \"svm\", and \"prediction\", and the last cluster focusing on \"reliability\" and \"complexity\". According to [PERSON] [66], the most frequently used keywords were \"artificial neural network\", \"runoff\", \"neural networks\", \"forecasting\", \"hydrological modeling\", \"artificial  
Figure 17: Thematic Evolution of Keywords Plus. intelligence\", \"stream flow\" and \"prediction\". Comparing our results with the existing literature, there are similarities for the majority of keywords, but at the same time, there are differences due to the analyzed timespan or the domain the articles focusing on. Figure 19 illustrates a factorial analysis for the most representative 100 bigrams used in abstracts, grouped into two clusters.