# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 3 Neural Network-Based Forecasting Approaches
### Optimization Algorithms with ANN-Based Forecasting Approaches  
The authors of [29] developed the temporal difference GA-based reinforcement learning neural network model to predict and control two chaotic systems, i.e., the Henon map and the logistic map. The advantage of the proposed concept is that it can apply directly to control chaotic physical systems in real-world models. [PERSON] et al. proposed the GA for forecasting the Mackey-Glass chaotic time series, and the model showed better performance than the ANN and polynomials methods [47]. The proposed model may also be used to forecast any other chaotic systems. In [71], a modified bee evolution using a PSO-based chaotic neural network model was presented to predict the load in the power system. The proposed model's simulation results showed better outcomes than the PSO algorithm used to develop the power system's proper planning and has good prospects. In [96], a hybrid approach using the chaotic self-adaptive PSO algorithm and BPNN was presented to forecast the polymers' gas solubility. The proposed model is reliable, accurate, and practicable for analyzing and designing polymer processing technology, compared to PSO-tuned BPNN models. It is to be noted that the proposed approach can also be further extended to tackle actual difficulties. The chaotic PSO tuned ANN model was presented in [102] to forecast air quality by predicting the particulate concentration. It is to be noted that this model can also be used to prove the meteorological condition of wind speed, which has a significant effect at urban intersections for specific matter concentrations. In [107], the researchers developed the improved GA for forecasting the synchronous parameters of chaotic time series to achieve higher accuracy and efficiency than GA alone. The authors of [115] proposed the modified BPNN based on chaotically optimized GA and simulated annealing algorithms to forecast electrical energy demand in a smart grid. It is to be noted that this concept can also be relevant to lowering the system's total operational costs and ensuring the effective and reliable functioning of next-generation power grids. [PERSON] et al. developed the extreme learning mechanism using RBFNN and SOM models to predict the multistep ahead time series of [PERSON]'s chaotic system [133]. It is to be noted that this proposed model can also combine with several behaviors to provide the best behavior. In [135], the researcher presented the GA and LS-based SVM method to control fractional-order systems, which achieved better effectiveness and feasibility than the conventional LS-based SVM. The authors of [139] proposed the principal component analysis using the chaotic immune PSO tuned GRNN for forecasting the corrosion of circulating cooling water in a petrochemical enterprise. The approach achieved better forecasting accuracy and convergence speed than the traditional PSO-tuned GRNN model. The advantage of the proposed model is that it can also be employed to forecast other nonlinear systems. In [147], the authors proposed the chaotic PSO algorithm for predicting the mobile location and achieved better location accuracy and faster convergence rate than such algorithms as those of Chan, Taylor, and PSO. [PERSON] et al. developed the fractal dimension-based EMD method and GA tuned BPNN model for predicting the wind speed in wind farms by considering the atmospheric motions' fractal feature [152]. The proposed models showed better performance than LSTM, GA tuned BPNN, and ensemble EMD-GA-BPNN. It is to be observed that this model can also require further study to optimize the computational time. It is also necessary to analyze the model on various time scales to decide the proposed models' suitability to wind speed series on any timescale. [PERSON] et al. proposed the SSA tuned CNN for predicting the short-term power of PV systems [158]. The presented algorithm's results showed better accuracy than the SSA tuned SVM and LSTM methods.