# A Review on Recent Advancements in FOREX Currency Prediction
## 7 Discussion and Research Aperture  
After reviewing 39 research papers, we saw that popular FOREX market predictive approaches in recent years included neural networks, different optimization techniques, and pattern-based approaches, whereas regression methods, SVM models, and chaos theory were the least popular. There are also some papers that did not follow any aforementioned approaches. Figure 8 shows the popularity of the algorithms according to our reviewed papers.  
As the unsupervised methods were out of the scope of this research, we cannot compare supervised methods with unsupervised methods. Additionally, many algorithms, such as shrinkage, subset selection, re-sampling methods, and discriminant analysis have not been explored by any of the researchers in recent times. Only a few researchers have shown interest in working with methods such as regression, chaos theory, and SVM-based approaches. Most of the regression methods use a support vector regressor (SVR) for prediction purposes. Some of the researchers used a SVM with another genetic algorithms. Some of them used chaos theory coupled with spline regression. The Markov model, the dynamic conditional co-relation, dynamic model averaging, recurrent reinforcement learning, etc., were chosen by many of the researchers. Some of them used a combination of some predictive statistical approaches for a better performance.  
Natural language processing (NLP) provided quite good results with models that used semantic analysis. However, most of the researchers have not shown too much interest in NLP. Recent years have seen only minimal implementation of NLP in the FOREX prediction field. Most of the model was based on news headline recognition using semantic analysis. However, there is a great chance of prediction disasters of these models if false news spreads. That is because these models will analyze and predict according to the given news. However, if the time series of the actual currency rates are applied with semantic analysis, the models will be more secure and will not be affected by false news. NLP has a huge potential to even compete with neural networks. Thus, in future the combination of NLP and neural network can be applied for FOREX rate prediction.  
The neural networks with different optimization techniques have seen a huge rise in research interest due to their way of working with greater prediction accuracy. Even though the NN has seen a rise in interest, many neural network algorithms, such as the modular neural network, sequence-to-sequence model, deep belief network, deep feed forward network, deconvolutional network, single multiplicative neural network, and spiking neural network were not even tested in FOREX price prediction within the stipulated time. The group method of data handling (GMDH), the world's first deep learning architecture, was not used by any researchers and the general regression neural network (GRNN) was rarely exploited in this time. The neural network has proved its worth  
Figure 8: Popularity of the algorithms.  
for many years now. Most of the high accuracy in predicting time series has been achieved by using neural networks. Each neural network is effective for a different kind of situation. Some perform better in classification; some in normalization. Some work great for predicting time series, whereas some perform average in any kind of situation. The challenge is to use these algorithms efficiently depending on the situation and problem type. Thus, several algorithms can be used in a single model to have a supportive algorithm for different situations. But many algorithms are yet to be explored and many hybrid models are yet to be built.