## 4 Discussion and Limitations
### Bibliometric Research Summary  
The bibliometric approach presented in this paper evaluated the development of time series forecasting and AI domains from the beginning of the period in which papers have been written and indexed in the WoS database, including the first paper published in 1997, which had an impact if we take into consideration the number of citations. Thanks to technological and scientific developments, it became easier to investigate the topics, and a positive trend was observed in the number of publications, especially in recent years. As observed during the bibliometric analysis process, a series of results, such as the most impactful sources, were also identified in similar studies from the field, underlining once more the stability of the selected research field over time. To demonstrate the outcome, the research addressed several scientific questions in the first chapter of the paper, to which, during the analysis, answers have been provided. First, it was observed that the collaboration network among the 30 most representative authors could be presented in five different clusters, each one focusing on different topics related to time series forecasting and artificial intelligence. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] explored hydrological time series prediction. Carpinteiro O.A.S., Pinheiro C.A.M., [PERSON], and [PERSON] evaluated the time series and artificial intelligence algorithms for financial predictions. [PERSON], [PERSON], [PERSON],[PERSON], and [PERSON] focused on traffic accident loss and air pollution estimations, while [PERSON], [PERSON], and [PERSON] presented Istanbul stock market prediction using time series algorithms. [PERSON], [PERSON], [PERSON], and [PERSON] described smart greenhouse methods for the evaluation of low-power devices. Second, the main topics extracted during the scientific research present the methods and domains where time series and artificial intelligence have been successfully implemented. The algorithms that have been implemented often were \"lstm\", \"neural networks\", \"support vector regression\", \"ARIMA\", \"particle swarm optimization\", and \"regression\", with great success especially in the hydrological, financial, and environmental fields, where the prediction of future values stands out as a key factor in the evolution of these domains. In terms of the most representative countries based on the number of publications and citations, the following countries were ranked at the top: China with 74 articles and 2415 citations, followed by Spain with 17 documents and 144 citations, the USA with 16 articles and 421 citations, Brazil with 13 documents and 195 citations, Italy with 10 documents and 108 citations, India with 9 papers and 259 citations, Iran with 9 articles and 205 citations, Korea with 9 documents and 17 citations, the UK with 8 articles and 75 citations, and Saudi Arabia with 7 papers and 52 citations. The 10 most cited documents explore the implementation of new methods of time series and artificial intelligence using adaptive neural-based fuzzy systems or genetic programming for monthly river flow discharges, oil production estimation using LSTM, and prediction of the number of tourists with ARIM or [PERSON]'s Model of Artificial Neural Networks. At the same time, detailed analyses were provided for electricity, energy consumption, and urban traffic flow, implementing multiple methods in order to obtain high accuracy and a reduced error rate. The sources with the highest impact based on the number of publications were IEEE Access with 11 documents, Energies with 9 articles, Mathematics with 7 papers, Applied Sciences-Basel with 6 papers, and Applied Energy, Energy, Knowledge-Based Systems, Sensors, and Sustainability with 5 documents each.