## 1 Introduction
[10] published an analysis of the implementation of artificial neural networks in time series forecasting problems, evaluating the possibility of the dendritic neuron model to identify outliers and their impact. The model has been tested on closing prices from German, Spanish, and Italian stock exchanges for a specific timespan, resulting in an algorithm that is able to accurately predict the values even when outliers exist in the dataset. [PERSON] et al. [11] described the existing risks on the market, together with the economic pressures and how technological developments, time series forecasting, and artificial intelligence could improve scientific productivity and business environments. Multiple algorithms have been developed, such as particle swarm optimization, genetic algorithm, and quantum optimization, resulting in a hybrid model between neutrosophic time series and AI models as an optimal solution. Furthermore, Dynamic Bayesian Networks (DBNs), included in the family of probabilistic graphical models (PGMs), have been used to model the evolution of parameters described as a time series, with a series of applications in the agricultural field, such as digital agriculture [12], and in transportation [13; 14; 15]. The purpose of the present bibliometric analysis was to evaluate the academic development of time series forecasting and AI domains and to understand the evolution of the algorithms and the level of inclusivity of the models. Thus, a bibliometric analysis has been conducted for a comprehensive overview of the main authors, affiliations, journals, international collaboration, thematic evolution, main contributors, and most used keywords, also taking into account the existing bibliometric analyses by the academic community [16; 17; 18; 19]. According to [PERSON] and [PERSON] [17], a bibliometric analysis is different from a review analysis that focuses on the main findings in the investigated field; the bibliometric overview explores the structural elements of the topic, extracting the main factors of time series forecasting and AI. In order to extract representative information from the research, this paper will focus on answering the following scientific questions:  
* SQ1: Are the authors contributing to this field likely to collaborate? If yes, what does the collaboration map look like? * SQ2: What are the most relevant themes discussed in the time series and artificial intelligence topics? * SQ3: Which countries are the most representative, taking into account the number of publications and citations? * SQ4: What are the outcomes of the 10 most cited articles? * SQ5: What are the sources with the highest impact based on the number of articles published? * SQ6: What are the main keywords included in the thematic graphical representation? As was already mentioned above, a detailed investigation of the 10 most cited papers will be carried out for a better understanding of the time series and AI domains. Furthermore, the time distribution of the publications included in the dataset is crucial in topic analysis evolution. The decision to conduct a bibliometric analysis for evaluating the time series and AI domains is in accordance with the existing literature, focusing on extracting, in a proper manner, the main topics, key contributors, collaboration map, main research institutions, and the evolution of the research themes [16; 20]. The bibliometric approach delves deeply into the analysis of the dataset using indicators such as the H-index or G-index, which will be further explained, together with graphical representations of Bradford's Law or Lotka's Law. Due to the nature of the scientific question, quantitative analysis is mandatory, evaluating the 10 most representative papers from the time series forecasting and AI fields.