## 3 Overview of Results
### Mixed Analysis
The blue cluster contains terms referring to time series and AI algorithms: \"AI techniques\", \"deep learning\", \"memory lstm\", \"energy consumption\", \"regression svr\", \"machine learning\", \"forecasting accuracy\", \"convolutional neural\", and \"time series\", while the red cluster, which is smaller, contains information about the time series and AI error metrics, such as \"percentage error\", \"error rmse\", \"squared error\", \"absolute error\", and \"error map\". [PERSON] et al. [43] evaluated the time series forecasting and modeling domain using a bibliometric analysis. The most relevant keywords discovered were divided into multiple groups: \"mean square error\", \"root mean square errors\", and \"mean absolute percentage error\", while the second group focused on \"deep neural networks\", \"time series forecasting\", \"prediction\", \"long short-term memory\", and \"artificial neural network\". [PERSON] et al. [67] investigated the applied artificial intelligence of an international journal using a bibliometric approach, discovering that \"model\", \"system\", \"neural networks\", \"artificial neural networks\", and \"genetic algorithms\" were the most representative terms. Our outcome correlates with the existing literature results, which confirm that the approach was correct, but at the same time, there were significant differences, which makes sense, due to the database differences, time period, and fields that were investigated. Figure 20 presents the 30 most relevant authors, grouped into five clusters. The most representative cluster is colored in blue, and it contains six authors: [PERSON], [PERSON].,  
Figure 18: Factorial Analysis of Keywords Plus. [PERSON], [PERSON], [PERSON], and [PERSON]. The focus of the authors was on AI algorithms for hydrological time series analysis, using parallel and swarm intelligence methods, signal decomposition, twin support vector machine, and river flow time series analysis using artificial neural networks for humid and semi-humid regions [68; 69; 70; 71]. The second cluster, colored in red, is represented by four authors: [PERSON], [PERSON], [PERSON], and [PERSON]. The main topics discussed by the researchers incorporated financial prediction using support vector regression, improving the accuracy of fuzzy forecasting of fuzzy c-means, explaining the rough sets, and testing time series algorithms for improving the prediction rate [9; 72; 73; 74]. The third group, delimited in green, is formed by five authors: [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], who focused mainly on predicting traffic accident economic loss and air pollution, wind speed prediction using defuzzificaition, and multi-objective optimization methods, energy consumption forecasting, and air quality prediction with nonlinear robust outliers and fuzzy sets [75; 76; 77; 78]. The fourth cluster, highlighted in purple, contains three authors: [PERSON], [PERSON], and [PERSON], who explored Istanbul stock market prediction using fuzzy time series methods, ARMA, artificial neural networks, and fuzzy time-series evaluation [79; 80; 81]. The fifth cluster, colored in yellow, is formed by four authors: [PERSON], [PERSON], [PERSON], and [PERSON] The main subjects explored by these authors were machine learning models for operational smart greenhouses, evaluation of smart greenhouses, evolution of low-power devices, and the investigation of data generated by intelligent climate control of greenhouses [82; 83; 84]. Based on the mixed analysis, the major topics discussed in the analyzed papers were related to artificial neural network algorithms, deep learning models, long short-term memory algorithms, random forest, support vector machines, recurrent neural networks, network CNN, explainable AI, vector regression, and support vector regression. At the same time, the main steps of AI and ML implementation were presented, such as data processing, learning methods from historical data, forecasting, and accuracy estimation.