# A Review on Recent Advancements in FOREX Currency Prediction
## 6 Brief Review of FOREX Price Prediction Techniques
### Chaos Theory  
[PERSON], [PERSON], and [PERSON] [69] proposed a three-stage hybrid prediction system that is capable of forecasting the time series successfully. They implemented two different models. First, with chaos, the multilayer perceptron (MLP) and multi-objective particle swarm optimization (MOPSO). Another model was chaos, multilayer perceptron (MLP), and a non-dominated sorting genetic algorithm (NSGA-II). In the first model, they prepared the dataset using minimum lag and minimum embedding dimensions in the first stage. In the second stage, the result produced by stage 1 was fed to a multilayer perceptron (MLP). In stage 3, they tested wheather the chaos was present or not. If chaos was found, the system fed the data to a MOPSO trained auto-regressive model. Otherwise, the polynomial regression was employed in the system. The second model is similar to the first model in the stage 1 and stage 2 but in stage 3 the MOPSO is replaced with NSGA-II. They used JPY/USD, GBP/USD, and EUR/USD currency pairs and gold price (USD) as dataset. They used [PERSON]'s inequality coefficient to find the closeness of the prediction done by their system. Additionally, to figure out whether the predictions were statistically significant or not, they used the Diebold-Mariano (DM) test. According to their result, their proposed second model with chaos, MLP and NSGA-II, outperformed the following hybrids: (i) chaos-MLP-MOPSO, (ii) chaos-GMDH, (iii) chaos-GRNN, (iv) chaos-NSGA-II, (v) chaos-MOPSO, (vi) chaos-PSO, (vii) chaos-MLP-PSO, (viii) chaos-TreeNet, (ix) chaos-MARS, (x) chaos-CART-EB, (xi) chaos-CART, (xii) chaos-RFTE, and (xiii) chaos-LASSO. [PERSON] [70] proposed a model for financial prediction based on a chaotic oscillatory multi-agent and neuro-computing system which he named COSMOS. This COSMOS system can be used as a predictor or trading strategy optimization system. The COSMOS forecaster constructed with chaotic FFBP-based supervised learning agents can be used for financial time series prediction, and a COSMOS trader constructed with chaotic RBF-based reinforcement learning agents can be used for trading strategy optimization. He used 2048 trading days consisting of: 84 FOREX currencies, nine cryptocurrencies, 19 commodities, and 17 financial indices' time-series as datasets. After the evaluation of the proposed system, he found that the system provided less than 1% forecast error for daily prediction of the last 500 days. Additionally, the system provided 8-13% monthly average returns and outperformed the time-driven strategy and price-driven strategy. He compared the overall performance of the system with the feed-forward back propagation neural network (FFBPN), a SVM, and a deep neural network with principal component analysis (DNN-PCA) for a one-year period. His proposed system outperformed FFBPN, SVM, and DNN-PCA algorithms by 7.49%, 3.03%, and 2.67% respectively. By using a new hybrid forecasting approach which involves chaos theory (chaos) and multivariate adaptive regression splines (MARS), foreign exchange rates were predicted by the authors of paper [71]. They used chaos theory for constructing state space and MARS for conferring accurate predictions. They tested their system with different types of chaos-based forecasting models for three major FOREX rates: JPY/USD, GBP/USD, EUR/USD, and they got the highest accurate predictions for those FOREX rates with the chaos + MARS approach. An innovative chaotic interval type-2 fuzzy neuro-oscillatory network (CIT2-FNN) was proposed by the authors of the paper [72], for worldwide financial prediction. The CIT2-FNNwas constructed by a chaotic discrete-time neural oscillator, namely, the Lee oscillator which serves as transient-fuzzy input neurons of the recurrent networks. The chaotic type-2 transient fuzzy logic (CT2 TFL) was proposed in their model to provide a truly type-2 fuzzy logic system (T2 FLS) with a chaotic transient fuzzy property to resolve the complexity problem.