# A Review on Recent Advancements in FOREX Currency Prediction
## 6 Brief Review of FOREX Price Prediction Techniques
### Pattern Based Methods  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. [73] proposed a new model for FOREX prediction called the elastic network model for FOREX market (ENMX) algorithm that is based on the behavior of macromolecules in dissolution. This model skips local minima and continues to produce unstable values like FOREX and also keeps its distance from an equilibrium. Their model can simulate 21 connected currency pairs and also represent the interactions between a user and system using random movements. They used root mean square error (RMSE) for the performance evaluation of their proposed model. They found that the pseudo-Voigt model is better suited for historical data; hence, they proposed pseudo-Voigt as an alternative distribution for their ENMX model. They compared their model with vector autoregression (VAR) and a driftless random walk model (RW); they found that ENMX performed better than those two compared models. The average RMSE for ENMX was reduced by almost 15% compared to both VAR and RW models. For average profit, it returned 15% more profit than VAR; for RW that number was 70%. [PERSON], [PERSON], and [PERSON] [74] proposed a system for short term prediction using recurrent reinforcement learning (RL). They used three hidden rectified linear unit neural network layers for their model and trained the model using Q-learning algorithm. They tried something different by using historical tick data to get new state and reward signals. They used only one currency pair EUR/USD as their dataset for training and evaluating their model. They ran their system over 10 times while applying different initial conditions. They found the system's profit results are dataset dependent. They also found that using the validation process, stable candidates can be selected. For normal testing they got an average profit of 78.5 +/ - 9.5 and for yearly average 11.2 +/ - 1.4. For the yearly testing result, they got an average total profit of 109.1 +/ - 17.8 and for a yearly average they got 15.6 +/ - 2.5. From the triannual testing result, they got an average total profit of 114.0 +/ - 19.6%, and for a yearly average their result was 16.3 +/ - 2.8%. [PERSON] and [PERSON] [75] proposed multidimensional string models which can be used for time series forecasting. They enhanced the 1-endpoint open string model with a 2-endpoint open string model combined with D2-brane. They showed how the statistics of the predictors can be changed by the new object properties and used them to model a variety of time series systems. They used four different currency pairs in real demo simulations for evaluating their system. After analyzing the result, they found improved net asset value for their proposed model. They also found that higher efficiency for the string models can be achieved by using longer string lengths. As a complementary tool, they also proposed angular momentum to analyze the stability of currency rates. [PERSON] and [PERSON] [76] proposed a model to predict the FOREX exchange rate using dynamic model averaging (DMA) and transformed models of general DMA. They applied this model on three currency pairs, JPY-USD, EUR-USD, and GBP-USD. Seventy percent of data was used for training the model and the remaining 30 percent was used for the evaluation. They evaluated their proposed model using mean square error (MSE) and mean absolute deviation (MAD). According to the results of their proposed model, they found that autoregression with four lags, also known as AR(4), and time-varying autoregression with four lags, known as the TVP-AR(4) structural break model, provides a better result in USD-JYP prediction. For the EUR-USD dataset the parsimonious model provided good results. They assumed that models that use random walkm which evolves coefficients, are best to use for this prediction. They found reliable consistency for DMA and DMS. [PERSON] [77] proposed a system that is based on the Markov chain model and is capable of predicting time series successfully.