# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 2 Review on Chaotic Time Series Forecasting
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Ref, Year** & **Country** & **Journal/ Conference** & \begin{tabular}{c} **Forecasting** \\ **Approach** \\ \end{tabular} & **Application** & \begin{tabular}{c} **Forecasting** \\ **Parameter(s)** \\ \end{tabular} & \
\begin{tabular}{c} **Comparison** \\ **Techniques** \\ \end{tabular} &
\begin{tabular}{c} **Performance** \\ **Measures** \\ \end{tabular} & **Data** \\ \hline
[41], 2005 & China & IEEE Conference & SVM & Market price & Exchange rate & ANN & MSE & – \\
[42], 2005 & Japan & IEEE Transactions on & Master-slave & FitzHugh-Nagumo & Chaotic behavior & – & Prediction error & – \\  & Circuits and Systems & synchronization & model, Chua’s & & & & \\  & & scheme & oscillator & & & & \\
[43], 2006 & Italy & Hydrological Sciences & RBPs & Henon map, Lorenz map, Soa-surface & Time series & – & CNSE &
\begin{tabular}{c} Mekong river in Thailand and \\ Laso, Chao \\ phaya river \\ in Thailand \\ \end{tabular} \\
[44], 2006 & China & IEEE Conference & Sigmoid and wavelet & ESN & Memory capacity & ESN predictor & NRMSE & – \\  & & function & & & & \\
[45], 2006 & Mexico & IEEE Conference & WNN & Lorenz system, Mackey-Glass model & Time series & BPNN & MSE & – \\
[46], 2006 & Spain & Physica D: & Discrete-time & Lorenz system & On-line parameter & Maythate’s technique, NMAE & – \\  & Nonlinear Phenomena & recursive update & & & & & \\
[47], 2006 & Iran & IEEE Conference & GA & Mackey-Glass model & Time series & ANN & NMSE &
\begin{tabular}{c} - \\ Weight \\ \end{tabular} \\
[48], 2006 & Canada & IEEE Conference & Time delay neural & Solar system & Number of dark spots & Weight & NMSE & Skybal, Solar elimination \\  & & network & & & & & \\  & & & & & & & \\  & & & & & & & \\
[49], 2007 & South Korea & IEEE Conference & Terminal sliding mode & Duffing, Lorenz systems & Tracking error & Classical sliding & MSE & – \\
[50], 2007 & China & IEEE Conference & Self-organizing Takagi and Sugeno-type FNN & Traffic system & Traffic flow & RBNN & RMSE & Zizhu Bridge \\
[51], 2007 & Greece & IEEE Conference & BPNN & Dido resonator & Time series & – & RMSE & – \\  & & & circuits & & & & \\
[52], 2007 & Iran & IEEE Conference & Co-evolutionary & Solar system & Sunspot number time & AR, Threshold & NMSE & Solar influences \\  & & & & series & AR model & data analysis & center, Belgium [1] \\
[53], 2007 & China & IEEE Conference & Evolving RNN & Lorenz, Logistic, Mackey-Glass, & Time series & LLNF, Bidirectional & NMSE, RMSE & Solar influences \\  & & & & Real-world sun & RNN & & \\  & & & spots series & & & & \\ \end{tabular}
\end{table} 
Table 1: Cont. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Ref., Year** & **Country** & **Journal/Conference** & **Forecasting** & **Application** & **Forecasting** & **Comparison** & **Performance** & **Data** \\  & & & & & & & **Parameter(s)** & **Techniques** & **Measures** & \\ \hline
[54], 2008 & China & IEEE Conference & \begin{tabular}{c} Distributed chaotic \\ fuzzy BBNN \\ Optimal BFNN \\ Applications \\ \end{tabular}  & Distribution network & Fault section & BPNN & – & – \\
[55], 2008 & China & Expert Systems with & \begin{tabular}{c} Optimal BFNN \\ Applications \\ \end{tabular}  & Signal deviation & Time series & Grey model, & MAD, MAPE, & \\
[56], 2008 & China & IEEE Conference & \begin{tabular}{c} Generalized EKF \\ NARX neural network \\ \end{tabular}  & \ \begin{tabular}{c} Lorenz system \\ Chaotic laser, \\ Chaotic laser, \\ chaotic laser \\ traffic \\ \end{tabular}  & Time series &
\begin{tabular}{c} MLP network \\ Time