## 2 Background
### A Brief Review of the Literature
On the other hand, [PERSON] and [PERSON] employed a hybrid system including PSO and the least square SVM to perform prediction processes [29]. Readers are also referred to [30; 31; 32; 33; 34; 35; 36] to get a better idea about using SVM in prediction problems. [PERSON] et al. carried out a study on the prediction of short-term traffic flow with an Artificial Intelligence-based approach. In this context, they used a prediction approach including the back-propagation neural network-niche genetic algorithm (NGA) [37]. In another study on predicting traffic flow, [PERSON] et al. predicted the 'lane-change trajectory by drivers' in urban traffic flow [38]. In the context of predicting urban traffic flow, [PERSON] et al. also developed an alternative approach and introduced the Fuzzy-Neural Model (FNM) for predicting traffic-flow in urban street networks [39]. [PERSON] and [PERSON] presented an alternative approach to predict traffic flow (hourly) by taking the effects of rainfall into consideration [40]. In detail, they employed a neuro-wavelet model including the stationary wavelet transform (SWT). As a three-technique hybrid system formation, [PERSON] et al. employed ensemble neural networks (ENN)-fuzzy logic (as Type 1-Type 2), and particle swarm optimization (PSO) to perform predictions related to the Mexican Stock Exchange [41]. [PERSON] et al. used the chaos over BP artificial neural networks (CBPANNs) system supported by the genetic algorithm to perform predictions on time series [42]. Briefly, they employed their system to predict wind power. In another study on predicting wind power and speed, [PERSON] et al. used a hybrid system based on the support vector regression (SVR) model, and cross correlation (CC) analysis. They supported their system with the cuckoo search (CS) and brainstorm optimization (BSO) algorithms [43] and applied the research to wind turbines running on a wind farm (China). [PERSON] et al. used a multi-resolution analysis along with the artificial wavelet neural network model to predict wind speed [44]. Briefly, the authors developed a prediction system that can be used in the context of renewable energy sources. The main objective of this study was to obtain an intelligent, alternative management approach for a micro-grid system that could use renewable energy within isolated and grid-connected power systems [44]. [PERSON] used a recurrent neural networks system, trained thanks to the technique of Cooperative Coevolution (CC), to predict chaotic time series [45]. [PERSON] and [PERSON] used artificial neural networks and weighted fuzzy membership functions (NEWFM) to perform predictions on the business cycle [46]. In detail, the authors used chaotic time series (adjusted leading composite index time series with coordinate embedding (time-delay)) for the NEWFM and predicted the flow of business in this way [46]. [PERSON] et al. employed artificial neural networks and, also, the adaptive neuro-fuzzy inference system to perform prediction operations on water levels that were tracked daily [47]. The theory of wavelet decomposition theory was also used within this research study. In their study, [PERSON] et al. employed differently structured dynamic neural networks (DNN) to predict chaotic systems like Mackey-Glass and Henon Map [48]. [PERSON] employed a wavelet neural network (WNN) to realize the predictions for monthly reservoir in-flow (from the data obtained from the basin of the Kemer Dam in Turkey) [49]. In detail, the system used here included discrete wavelet transform (DWT)-feed forward neural networks (FFNN), which are supported by the Levenberg-Marquardt optimization algorithm. [PERSON] et al. developed a dendritic neuron model (with dendritic functions and phase space reconstruction) for predictions over the financial time series of the Shanghai Stock Exchange Composite Index, the Deutscher Aktienindex, the DJI Average, and the N225 [50]. In a research study, [PERSON] et al.