## 2 Background
### A Brief Review of the Literature
used differential evolution (DE) supported by teaching-learning based optimization (TLBO) to predict some chaotic time series [51]. In relation to the prediction of chaotic time series, [PERSON] et al. employed the Takagi Sugeno Kang (TSK) second-order fuzzy system and ANFIS (artificial neural fuzzy inference system) [52]. [PERSON] et al. provided a study on the prediction of time series using a Back Propagation-oriented neural network model trained by adaptive differential evaluation (ADE) [53]. It was reported that the authors produced successful, improved results in their study. [PERSON] et al. used a hybrid system in their study to predict short term electricity prices [54]. In detail, they employed particle swarm optimization (PSO), wavelet transform (WT), and ANFIS for their research purposes. A hybrid system of the ANFIS-vortex optimization algorithm (VOA) was used by [PERSON] and [PERSON] to perform predictions over the time series of EEG. It was reported that the related system is successful enough in the prediction of EEG data [10]. A model of adaptive local linear (optimized) radial basis functional neural network (LLRBFNN) was employed by [PERSON] et al. to predict some financial time series [55]. It was reported in that study that LLRBFNNcan perform predictions successfully enough and performs better than some other alternative systems that have been considered. In another study on financial time series prediction, [PERSON] et al. built a hybrid model including three different solution elements [56]. They designed some three-stage hybrid models that employ chaos to construct the phase space in stage 1, the Multi-Layer Perceptron (MLP) network model in stage 2, and particle swarm optimization with a multi-objective mechanism (MOPSO) and the elitist non-dominated sorting genetic algorithm (NDSGA) in stage 3. Both long-term and short-term time series prediction were achieved by [PERSON] et al. by using a different approach [57]. In their study, they built a modular (structured) neural network (MNN) model with two methods: competitive clustering and winner-takes-all. Following the examination of works in the literature that have put more emphasis on the prediction of EEG time series (with a chaotic nature), the several works are explained briefly. In their study, [PERSON] et al. used radial basis function neural networks with the adaptive projective learning algorithm to predict some EEG time series [58]. In detail, they showed that the optimal choice of alpha parameters within the system can effectively predict EEG data and this depends on the correlation dimension of objective EEG time series. As a traditional use, [PERSON] employed the BP neural network model to perform some prediction operations over the EEG time series [59]. In the study, they also used brain electrical activity mapping (BEAM) as an alternative research approach. In a recent study by [PERSON] et al., both the convolution neural network (CNN) and the bi-directional recurrent neural network (BRNN) were employed to develop a universal predictor for both EEG and ECG time series [60]. The related system showed positive performances for predicting the chaotic time series by considering two medical data types: EEG and ECG. [PERSON] and [PERSON] used non-linear and autoregressive (AR) techniques to predict both EEG and simulated time series [61]. The study followed the objective of evaluating whether the techniques can distinguish chaotic time series from noisy ones and positive results were obtained. In another study, [PERSON] et al. used a third-order Volterra filter to predict chaotic time series including EEG data [62]. The findings obtained in the research showed that the used technique was better than the second-order Volterra filter and it was successful in predicting high dimensional chaotic EEG time series. [PERSON] et al. used two neural network models to perform prediction operations on an EEG time series in the context of a feature extraction approach for a brain-computer interface [63].