## 3 Overview of Results
### Most Cited Documents  
This section presents the 10 most representative papers in the time series forecasting and Artificial Intelligence domains based on the total number of citations. A short description of each article will be described below in order to observe and understand the main objective of the analysis, extracting the major topics and methods that have been implemented. Table 2 includes relevant information regarding the 10 most cited papers, such as the first author of the article, year when it was published, journal name, the number of researchers, region, total citations (TC), total citations per year (TCY), and normalized total citations (NTC). The NTC indicator presents importance mostly in the context of the  
Figure 11: Most Representative Universities. other papers included in the dataset, as it presents how many times an article succeeds in receiving more citations than the average number of citations received by the papers published in the same year as the analyzed paper and included in the dataset [53; 54]. AI and time series forecasting developed significantly in recent years, mainly thanks to the technological evolution. The most cited paper was published by [PERSON] [44] and has been cited 614 times, having four authors, a total citations per year (TCY) of 36.12, and a normalized total citation (NTC) of 1. The authors explored the hydrological sector, focusing on estimating hydropower reservoir management and optimal scheduling, using past data, implementing multiple models such as the autoregressive moving average (ARMA), adaptive neural-based fuzzy inference system (ANFIS), artificial neural networks (ANN), genetic programming (GP), and support vector machine (SVM). The algorithms were trained on monthly river flow discharges and, in order to classify the models, multiple statistical metrics were calculated, such as the Nash-Sutcliffe efficiency coefficient (E), coefficient of correlation (R), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The outcome showed that SVM, GP, and ANFIS were the algorithms with the highest accuracy based on various criteria that had been tested for training and validation processes. Oil production was forecasted with the long short-term memory (LSTM) neural network approach for production by fractured horizontal wells, using two case studies from Xinjiang oilfield, China. [PERSON] et al. [55] also included Particle Swarm Optimization (PSO) in order to optimize the LSTM algorithm. The results were compared with traditional neural network methods, conventional decline curves, and time-series solutions. The outcome demonstrated that LSTM outperformed the traditional algorithms. In total, eight authors contributed, obtaining a total of 241 citations, with total citations per year of 40.17 and a normalized total citation of 5.70. Solar power generated by photovoltaic panels plays a significant role in the electricity market, especially when fluctuations in the systems occur due to environmental factors. [PERSON] et al. [56] predicted solar power generation using AI, neural networks, LSTM, and a multilayer perceptron (MLP) network. The objective of the research was to accurately predict solar power production, facilitating the operation, scheduling, and management of related power systems. At the same time, the effect of power forecasting was explored. The data included in the analysis contained over 3640 h of operation-related data points extracted from grid-connected photovoltaic stations in China, which produced a total of 20 megawatts (MW). There were three authors that investigated the topic, receiving a total of 225 citations, with a value of 37.50 for TCY and 5.32 for NTC. The tourism sector plays a crucial role in the global economy, representing a priority for [PERSON], who evaluated multiple time series algorithms like exponential smoothing, [PERSON]'s Model of Artificial Neural Networks, and ARIMA. The objective of the research was to estimate tourism traffic based on historical data and to understand tourists' behavior.